Console log: 'Microsoft.ML.Predictor.Tests' from job 72045c46-d981-444c-a014-98518d7f5597 workitem 20d6367e-b50f-44b5-b59c-fd9a4d0ac98e (osx.15.arm64.open) executed on machine dci-macm1-build-198 running macOS-15.7 + export ML_TEST_DATADIR=/tmp/helix/working/99790869/p + ML_TEST_DATADIR=/tmp/helix/working/99790869/p + export MICROSOFTML_RESOURCE_PATH=/tmp/helix/working/99790869/w/AD3309DB/e + MICROSOFTML_RESOURCE_PATH=/tmp/helix/working/99790869/w/AD3309DB/e + sudo chmod -R 777 /tmp/helix/working/99790869/w/AD3309DB/e + sudo chown -R helix-runner /tmp/helix/working/99790869/w/AD3309DB/e + export PATH=/tmp/helix/working/99790869/p/dotnet-cli:/etc/helix-prep/venv/bin:/usr/bin:/bin:/usr/sbin:/sbin + PATH=/tmp/helix/working/99790869/p/dotnet-cli:/etc/helix-prep/venv/bin:/usr/bin:/bin:/usr/sbin:/sbin + export LD_LIBRARY_PATH=/opt/homebrew/opt/mono-libgdiplus/lib + LD_LIBRARY_PATH=/opt/homebrew/opt/mono-libgdiplus/lib + ls /usr/lib cron dsc_extractor.bundle dtrace dyld i18n ignition libffi-trampolines.dylib libgmalloc.dylib libipconfig.dylib libLeaksAtExit.dylib libMTLCapture.dylib libMTLToolsDiagnostics.dylib libobjc-trampolines.dylib libpcre2-8.dylib libpcre2-posix.dylib libRPAC.dylib libz.1.2.12.dylib log pam pkgconfig rpcsvc ruby sasl2 sqlite3 ssh-keychain.dylib swift system updaters usd zsh + ls /tmp/helix/working/99790869/w/AD3309DB/e CoverletSourceRootsMapping_Microsoft.ML.Predictor.Tests cs de es fr it ja ko libomp.dylib libSymSgdNative.dylib Microsoft.ApplicationInsights.dll Microsoft.Diagnostics.Runtime.dll Microsoft.DotNet.PlatformAbstractions.dll Microsoft.DotNet.RemoteExecutor.dll Microsoft.DotNet.XUnitExtensions.dll Microsoft.Extensions.DependencyModel.dll Microsoft.ML.Core.deps.json Microsoft.ML.Core.dll Microsoft.ML.Core.pdb Microsoft.ML.Core.xml Microsoft.ML.CpuMath.deps.json Microsoft.ML.CpuMath.dll Microsoft.ML.CpuMath.pdb Microsoft.ML.CpuMath.xml Microsoft.ML.Data.deps.json Microsoft.ML.Data.dll Microsoft.ML.Data.pdb Microsoft.ML.Data.xml Microsoft.ML.DataView.deps.json Microsoft.ML.DataView.dll Microsoft.ML.DataView.pdb Microsoft.ML.DataView.xml Microsoft.ML.Ensemble.deps.json Microsoft.ML.Ensemble.dll Microsoft.ML.Ensemble.pdb Microsoft.ML.Ensemble.xml Microsoft.ML.EntryPoints.deps.json Microsoft.ML.EntryPoints.dll Microsoft.ML.EntryPoints.pdb Microsoft.ML.EntryPoints.xml Microsoft.ML.FastTree.deps.json Microsoft.ML.FastTree.dll Microsoft.ML.FastTree.pdb Microsoft.ML.FastTree.xml Microsoft.ML.KMeansClustering.deps.json Microsoft.ML.KMeansClustering.dll Microsoft.ML.KMeansClustering.pdb Microsoft.ML.KMeansClustering.xml Microsoft.ML.LightGbm.deps.json Microsoft.ML.LightGbm.dll Microsoft.ML.LightGbm.pdb Microsoft.ML.LightGbm.xml Microsoft.ML.Maml.deps.json Microsoft.ML.Maml.dll Microsoft.ML.Maml.pdb Microsoft.ML.Maml.xml Microsoft.ML.Mkl.Components.deps.json Microsoft.ML.Mkl.Components.dll Microsoft.ML.Mkl.Components.pdb Microsoft.ML.Mkl.Components.xml Microsoft.ML.OneDal.deps.json Microsoft.ML.OneDal.dll Microsoft.ML.OneDal.pdb Microsoft.ML.OneDal.xml Microsoft.ML.Parquet.deps.json Microsoft.ML.Parquet.dll Microsoft.ML.Parquet.pdb Microsoft.ML.Parquet.xml Microsoft.ML.PCA.deps.json Microsoft.ML.PCA.dll Microsoft.ML.PCA.pdb Microsoft.ML.PCA.xml Microsoft.ML.Predictor.Tests.deps.json Microsoft.ML.Predictor.Tests.dll Microsoft.ML.Predictor.Tests.pdb Microsoft.ML.Predictor.Tests.runtimeconfig.json Microsoft.ML.Predictor.Tests.xml Microsoft.ML.Recommender.deps.json Microsoft.ML.Recommender.dll Microsoft.ML.Recommender.pdb Microsoft.ML.Recommender.xml Microsoft.ML.ResultProcessor.deps.json Microsoft.ML.ResultProcessor.dll Microsoft.ML.ResultProcessor.pdb Microsoft.ML.ResultProcessor.xml Microsoft.ML.StandardTrainers.deps.json Microsoft.ML.StandardTrainers.dll Microsoft.ML.StandardTrainers.pdb Microsoft.ML.StandardTrainers.xml Microsoft.ML.TestFramework.deps.json Microsoft.ML.TestFramework.dll Microsoft.ML.TestFramework.pdb Microsoft.ML.TestFramework.runtimeconfig.json Microsoft.ML.TestFramework.xml Microsoft.ML.TestFrameworkCommon.deps.json Microsoft.ML.TestFrameworkCommon.dll Microsoft.ML.TestFrameworkCommon.pdb Microsoft.ML.TestFrameworkCommon.runtimeconfig.json Microsoft.ML.TestFrameworkCommon.xml Microsoft.ML.Transforms.deps.json Microsoft.ML.Transforms.dll Microsoft.ML.Transforms.pdb Microsoft.ML.Transforms.xml Microsoft.Testing.Extensions.MSBuild.dll Microsoft.Testing.Extensions.Telemetry.dll Microsoft.Testing.Extensions.TrxReport.Abstractions.dll Microsoft.Testing.Extensions.VSTestBridge.dll Microsoft.Testing.Platform.dll Microsoft.TestPlatform.AdapterUtilities.dll Microsoft.TestPlatform.CommunicationUtilities.dll Microsoft.TestPlatform.CoreUtilities.dll Microsoft.TestPlatform.CrossPlatEngine.dll Microsoft.TestPlatform.PlatformAbstractions.dll Microsoft.TestPlatform.Utilities.dll Microsoft.VisualStudio.CodeCoverage.Shim.dll Microsoft.VisualStudio.TestPlatform.Common.dll Microsoft.VisualStudio.TestPlatform.MSTest.TestAdapter.dll Microsoft.VisualStudio.TestPlatform.MSTestAdapter.PlatformServices.dll Microsoft.VisualStudio.TestPlatform.ObjectModel.dll Microsoft.VisualStudio.TestPlatform.TestFramework.dll Microsoft.VisualStudio.TestPlatform.TestFramework.Extensions.dll Microsoft.VisualStudio.TestPlatform.TestFramework.Extensions.xml Newtonsoft.Json.dll Parquet.dll pl pt-BR ru runTests.sh runtimes System.CodeDom.dll System.Collections.Immutable.dll System.IO.Pipelines.dll System.Numerics.Tensors.dll System.Text.Encodings.Web.dll System.Text.Json.dll System.Threading.Channels.dll testhost.dll tr xunit.abstractions.dll xunit.assert.dll Xunit.Combinatorial.dll xunit.core.dll xunit.execution.dotnet.dll xunit.runner.json xunit.runner.visualstudio.testadapter.dll zh-Hans zh-Hant + export KMP_DUPLICATE_LIB_OK=TRUE + KMP_DUPLICATE_LIB_OK=TRUE + otool -L /tmp/helix/working/99790869/w/AD3309DB/e/runtimes/osx-x64/native/lib_lightgbm.dylib /tmp/helix/working/99790869/w/AD3309DB/e/runtimes/osx-x64/native/lib_lightgbm.dylib: @rpath/lib_lightgbm.dylib (compatibility version 0.0.0, current version 0.0.0) @rpath/libomp.dylib (compatibility version 5.0.0, current version 5.0.0) /usr/lib/libc++.1.dylib (compatibility version 1.0.0, current version 1500.65.0) /usr/lib/libSystem.B.dylib (compatibility version 1.0.0, current version 1319.100.3) + export DYLD_LIBRARY_PATH=/tmp/helix/working/99790869/w/AD3309DB/e: + DYLD_LIBRARY_PATH=/tmp/helix/working/99790869/w/AD3309DB/e: + export DYLD_FALLBACK_LIBRARY_PATH=/tmp/helix/working/99790869/w/AD3309DB/e: + DYLD_FALLBACK_LIBRARY_PATH=/tmp/helix/working/99790869/w/AD3309DB/e: + ./runTests.sh ----- start Mon Sep 21 06:24:06 PDT 2026 =============== To repro directly: ===================================================== pushd . dotnet exec --roll-forward Major --runtimeconfig Microsoft.ML.Predictor.Tests.runtimeconfig.json --depsfile Microsoft.ML.Predictor.Tests.deps.json /tmp/helix/working/99790869/p/xunit-runner/tools/netcoreapp2.0/xunit.console.dll Microsoft.ML.Predictor.Tests.dll -notrait Category=SkipInCI -xml testResults.xml popd =========================================================================================================== /private/tmp/helix/working/99790869/w/AD3309DB/e /private/tmp/helix/working/99790869/w/AD3309DB/e xUnit.net Console Runner v2.9.3+9712244020 (64-bit .NET 8.0.16)  Discovering: Microsoft.ML.Predictor.Tests (method display = ClassAndMethod, method display options = None)  Discovered: Microsoft.ML.Predictor.Tests (found 112 test cases)  Starting: Microsoft.ML.Predictor.Tests (parallel test collections = on [8 threads], stop on fail = off)  Microsoft.ML.RunTests.TestConcurrency.TestCVWithLRParallel [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.CmdLine.CmdParsingSingle [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestParallelFasttreeInterface.CheckFastTreeParallelInterface [SKIP]  'checker' is not a valid value for the 'parag' argument in FastTree  Microsoft.ML.RunTests.TestConcurrency.TestBootstrapWithLRParallel [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.CmdLine.CmdParsingBasic [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestResultProcessor.RPProcessClassifierRegressorTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestResultProcessor.RPSingleClassifierTestWithSpace [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestResultProcessor.RPMulticlassifierTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestResultProcessor.RPSingleClassifierTestWIthEmptyLines [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestBaselines.AAACompareBaselines [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.CmdIndenterTests.TestCmdIndenter [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.Global.AssertHandlerTest [SKIP]  Disabled Starting test: Microsoft.ML.RunTests.TestTransposer.TransposerTest Starting test: Microsoft.ML.RunTests.CmdLineReverseTests.NewTest Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmNoBiasTest Starting test: Microsoft.ML.RunTests.TestIniModels.TestGamRegressionIni Starting test: Microsoft.ML.RunTests.TestGamPublicInterfaces.TestGamDirectInstantiation Starting test: Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths Finished test: Microsoft.ML.RunTests.TestGamPublicInterfaces.TestGamDirectInstantiation with memory usage 77,742,080.00 and max memory usage 0.00 Finished test: Microsoft.ML.RunTests.CmdLineReverseTests.NewTest with memory usage 84,180,992.00 and max memory usage 0.00 Starting test: Microsoft.ML.RunTests.CmdLineReverseTests.ArgumentParseTest Finished test: Microsoft.ML.RunTests.CmdLineReverseTests.ArgumentParseTest with memory usage 93,945,856.00 and max memory usage 0.00 Finished test: Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths with memory usage 98,369,536.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths(directory: "absolute") [PASS] Starting test: Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths  Output:  Test ResultProcessorWithAbsolutePaths: completed normally: passed Finished test: Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths with memory usage 103,268,352.00 and max memory usage 0.00 Starting test: Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths  Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths(directory: "with spaces") [PASS]  Output:  Test ResultProcessorWithAbsolutePaths: completed normally: passed Finished test: Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths with memory usage 110,264,320.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths(directory: "with {braces}") [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths  Test ResultProcessorWithAbsolutePaths: completed normally: passed Finished test: Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths with memory usage 114,769,920.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestResultProcessor.ResultProcessorWithAbsolutePaths(directory: "with {unmatched brace") [PASS]  Output:  Test ResultProcessorWithAbsolutePaths: completed normally: passed  Microsoft.ML.RunTests.TestResultProcessor.RPSingleClassifierTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestTransposer.TransposerTest with memory usage 120,356,864.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestTransposer.TransposerTest [PASS]  Output:  Test TransposerTest: aborted: passed Starting test: Microsoft.ML.RunTests.TestTransposer.TransposerSaverLoaderTest Finished test: Microsoft.ML.RunTests.TestTransposer.TransposerSaverLoaderTest with memory usage 135,610,368.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestTransposer.TransposerSaverLoaderTest [PASS]  Output:  Wrote row-wise data, schema, and metadata data view in 440926 bytes    Wrote A data view in 60607 bytes    Wrote B data view in 364868 bytes    Wrote C data view in 6491 bytes    Wrote D data view in 11587 bytes    Wrote E data view in 1036 bytes    Wrote F data view in 3875 bytes    Test TransposerSaverLoaderTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmNoBiasTest with memory usage 141,869,056.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmNoBiasTest [PASS]  Output:  Running 'LdSvm' on 'breast-cancer'  Running as: TrainTest tr=LdSvm{iter=1000 bias=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=LdSvm{iter=1000 bias=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Starting test: Microsoft.ML.RunTests.TestPredictors.PAVCalibratorLinearSvmTest    Warning:  Skipped 16 rows with missing feature/label values    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 216 | 23 | 0.9038    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9558 | 0.9497 |    OVERALL 0/1 ACCURACY: 0.951684    LOG LOSS/instance: 0.251640    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.730579    AUC: 0.969024      OVERALL RESULTS    ---------------------------------------    AUC: 0.969024 (0.0000)    Accuracy: 0.951684 (0.0000)    Positive precision: 0.955752 (0.0000)    Positive recall: 0.903766 (0.0000)    Negative precision: 0.949672 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.251640 (0.0000)    Log-loss reduction: 0.730579 (0.0000)    F1 Score: 0.929032 (0.0000)    AUPRC: 0.965033 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:07 PM Time elapsed(s): 0.613      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer-out.txt  Output matches baseline: 'LdSvm/LDSVM-nob-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'LdSvm/LDSVM-nob-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-TrainTest-breast-cancer.txt  Output matches baseline: 'LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 216 | 23 | 0.9038    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9558 | 0.9497 |    OVERALL 0/1 ACCURACY: 0.951684    LOG LOSS/instance: 0.251640    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.730579    AUC: 0.969024      OVERALL RESULTS    ---------------------------------------    AUC: 0.969024 (0.0000)    Accuracy: 0.951684 (0.0000)    Positive precision: 0.955752 (0.0000)    Positive recall: 0.903766 (0.0000)    Negative precision: 0.949672 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.251640 (0.0000)    Log-loss reduction: 0.730579 (0.0000)    F1 Score: 0.929032 (0.0000)    AUPRC: 0.965033 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:07 PM Time elapsed(s): 0.018      Suffix of length 34 compared against sequence of length 38  Running 'LdSvm' on 'breast-cancer'  Running as: CV tr=LdSvm{iter=1000 bias=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-CV-breast-cancer.txt} threads-  maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 8 rows with missing feature/label values    Training calibrator.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 8 rows with missing feature/label values    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 126 | 8 | 0.9403    negative || 12 | 208 | 0.9455    ||======================    Precision || 0.9130 | 0.9630 |    OVERALL 0/1 ACCURACY: 0.943503    LOG LOSS/instance: 0.262484    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.725722    AUC: 0.980801    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 103 | 2 | 0.9810    negative || 36 | 188 | 0.8393    ||======================    Precision || 0.7410 | 0.9895 |    OVERALL 0/1 ACCURACY: 0.884498    LOG LOSS/instance: 0.231456    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.743810    AUC: 0.984396      OVERALL RESULTS    ---------------------------------------    AUC: 0.982598 (0.0018)    Accuracy: 0.914001 (0.0295)    Positive precision: 0.827025 (0.0860)    Positive recall: 0.960625 (0.0203)    Negative precision: 0.976218 (0.0133)    Negative recall: 0.892370 (0.0531)    Log-loss: 0.246970 (0.0155)    Log-loss reduction: 0.734766 (0.0090)    F1 Score: 0.885366 (0.0411)    AUPRC: 0.972638 (0.0101)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:08 PM Time elapsed(s): 0.634      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer-out.txt  Output matches baseline: 'LdSvm/LDSVM-nob-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer-rp.txt  Output matches baseline: 'LdSvm/LDSVM-nob-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-nob-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-nob-CV-breast-cancer.txt  Output matches baseline: 'LdSvm/LDSVM-nob-CV-breast-cancer.txt'  Test BinaryClassifierLDSvmNoBiasTest: completed normally: passed  Test BinaryClassifierLDSvmNoBiasTest is using osx-arm64 configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.PAVCalibratorLinearSvmTest with memory usage 131,874,816.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.PAVCalibratorLinearSvmTest [PASS]  Output:  Running 'LinearSVM' on 'breast-cancer'  Running as: TrainTest tr=LinearSVM{iter=100 lambda=0.03} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt} cali=PAV  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=LinearSVM{iter=100 lambda=0.03} cali=PAV dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter) Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierSymSgdTest    Training calibrator.    PAV calibrator: piecewise function approximation has 8 components.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 226 | 13 | 0.9456    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9617 | 0.9710 |    OVERALL 0/1 ACCURACY: 0.967789    LOG LOSS/instance: 0.084588    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.909435    AUC: 0.995797      OVERALL RESULTS    ---------------------------------------    AUC: 0.995797 (0.0000)    Accuracy: 0.967789 (0.0000)    Positive precision: 0.961702 (0.0000)    Positive recall: 0.945607 (0.0000)    Negative precision: 0.970982 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.084588 (0.0000)    Log-loss reduction: 0.909435 (0.0000)    F1 Score: 0.953586 (0.0000)    AUPRC: 0.991453 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:08 PM Time elapsed(s): 0.101      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt  Output matches baseline: 'LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt  Output matches baseline: 'LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt  Output matches baseline: 'LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 226 | 13 | 0.9456    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9617 | 0.9710 |    OVERALL 0/1 ACCURACY: 0.967789    LOG LOSS/instance: 0.084588    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.909435    AUC: 0.995797      OVERALL RESULTS    ---------------------------------------    AUC: 0.995797 (0.0000)    Accuracy: 0.967789 (0.0000)    Positive precision: 0.961702 (0.0000)    Positive recall: 0.945607 (0.0000)    Negative precision: 0.970982 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.084588 (0.0000)    Log-loss reduction: 0.909435 (0.0000)    F1 Score: 0.953586 (0.0000)    AUPRC: 0.991453 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:08 PM Time elapsed(s): 0.009      Suffix of length 34 compared against sequence of length 39  Running 'LinearSVM' on 'breast-cancer'  Running as: CV tr=LinearSVM{iter=100 lambda=0.03} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt} threads- cali=PAV  maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)    Training calibrator.    PAV calibrator: piecewise function approximation has 6 components.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)    Training calibrator.    PAV calibrator: piecewise function approximation has 6 components.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 128 | 6 | 0.9552    negative || 7 | 213 | 0.9682    ||======================    Precision || 0.9481 | 0.9726 |    OVERALL 0/1 ACCURACY: 0.963277    LOG LOSS/instance: Infinity    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): -Infinity    AUC: 0.994233    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 97 | 8 | 0.9238    negative || 2 | 222 | 0.9911    ||======================    Precision || 0.9798 | 0.9652 |    OVERALL 0/1 ACCURACY: 0.969605    LOG LOSS/instance: 0.220291    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.756168    AUC: 0.997491      OVERALL RESULTS    ---------------------------------------    AUC: 0.995862 (0.0016)    Accuracy: 0.966441 (0.0032)    Positive precision: 0.963973 (0.0158)    Positive recall: 0.939517 (0.0157)    Negative precision: 0.968910 (0.0037)    Negative recall: 0.979627 (0.0114)    Log-loss: Infinity (NaN)    Log-loss reduction: -Infinity (NaN)    F1 Score: 0.951327 (0.0003)    AUPRC: 0.991949 (0.0025)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:08 PM Time elapsed(s): 0.087      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt  Output matches baseline: 'LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt  Output matches baseline: 'LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt  Output matches baseline: 'LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt'  Test PAVCalibratorLinearSvmTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.LightGBMPreviousModelBaselineTest [SKIP]  LightGBM is 64-bit only  Microsoft.ML.RunTests.TestPredictors.RegressorSdcaTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierSymSgdTest with memory usage 135,217,152.00 and max memory usage 0.00 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBestDiverseSelectorTest  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierSymSgdTest [FAIL]  Assert.Equal() Failure: Values differ  Expected: 0  Actual: 2  Stack Trace:  /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs(221,0): at Microsoft.ML.RunTests.BaseTestBaseline.Done()  /Users/runner/work/1/s/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs(294,0): at Microsoft.ML.RunTests.TestPredictors.BinaryClassifierSymSgdTest()  at System.RuntimeMethodHandle.InvokeMethod(Object target, Void** arguments, Signature sig, Boolean isConstructor)  at System.Reflection.MethodBaseInvoker.InvokeWithNoArgs(Object obj, BindingFlags invokeAttr)  Output:  Running 'SymSGD' on 'breast-cancer'  Running as: TrainTest tr=SymSGD{nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer.txt} norm=no  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=SymSGD{nt=1} norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Data fully loaded into memory.    Initial learning rate is tuned to 100.000000    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 227 | 12 | 0.9498    negative || 8 | 436 | 0.9820    ||======================    Precision || 0.9660 | 0.9732 |    OVERALL 0/1 ACCURACY: 0.970717    LOG LOSS/instance: Infinity    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): -Infinity    AUC: 0.996108      OVERALL RESULTS    ---------------------------------------    AUC: 0.996108 (0.0000)    Accuracy: 0.970717 (0.0000)    Positive precision: 0.965957 (0.0000)    Positive recall: 0.949791 (0.0000)    Negative precision: 0.973214 (0.0000)    Negative recall: 0.981982 (0.0000)    Log-loss: Infinity (0.0000)    Log-loss reduction: -Infinity (0.0000)    F1 Score: 0.957806 (0.0000)    AUPRC: 0.991948 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:08 PM Time elapsed(s): 0.018      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SymSGD/osx-arm64/SymSGD-TrainTest-breast-cancer-out.txt  Output matches baseline: 'SymSGD/SymSGD-TrainTest-breast-cancer-out.txt'  Saving summary with: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer-summary.txt}  Saving predictor summary    Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer-summary.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SymSGD/osx-arm64/SymSGD-TrainTest-breast-cancer-summary.txt  Output matches baseline: 'SymSGD/SymSGD-TrainTest-breast-cancer-summary.txt'  *** Failure #1: Baseline file not found: /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SymSGD/SymSGD-TrainTest-breast-cancer-rp.txt  Void Fail(System.String, System.Object[]) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs 244  Boolean CheckBaseFile(System.String) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs 956  Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs 425  Boolean CheckEqualityNormalized(System.String, System.String, System.String, Int32, NumberParseOption) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs 397  Void Run(RunContext, Int32, NumberParseOption) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestPredictorsMaml.cs 212  Void Run_TrainTest(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Boolean, Boolean, Int32, NumberParseOption) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestPredictorsMaml.cs 399  Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestPredictorsMaml.cs 365  Void BinaryClassifierSymSgdTest() /Users/runner/work/1/s/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs 293  System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0  System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0  System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task b__1() 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0  System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215  Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0  System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0  Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0  System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163  System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0  System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149  Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206  Void MoveNext() 0  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185  Void MoveNext() 0  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250  Void InnerInvoke() 0  Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0  Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0  Boolean Dispatch() 0  Void WorkerThreadStart() 0  Void StartCallback() 0  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SymSGD/osx-arm64/SymSGD-TrainTest-breast-cancer.txt  Output matches baseline: 'SymSGD/SymSGD-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 227 | 12 | 0.9498    negative || 8 | 436 | 0.9820    ||======================    Precision || 0.9660 | 0.9732 |    OVERALL 0/1 ACCURACY: 0.970717    LOG LOSS/instance: Infinity    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): -Infinity    AUC: 0.996108      OVERALL RESULTS    ---------------------------------------    AUC: 0.996108 (0.0000)    Accuracy: 0.970717 (0.0000)    Positive precision: 0.965957 (0.0000)    Positive recall: 0.949791 (0.0000)    Negative precision: 0.973214 (0.0000)    Negative recall: 0.981982 (0.0000)    Log-loss: Infinity (0.0000)    Log-loss reduction: -Infinity (0.0000)    F1 Score: 0.957806 (0.0000)    AUPRC: 0.991948 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:08 PM Time elapsed(s): 0.007      Suffix of length 34 compared against sequence of length 39  Running 'SymSGD' on 'breast-cancer'  Running as: CV tr=SymSGD{nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-CV-breast-cancer.txt} norm=no threads-  maml.exe CV tr=SymSGD{nt=1} threads=- norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Not adding a normalizer.    Data fully loaded into memory.    Initial learning rate is tuned to 100.000000    Not training a calibrator because it is not needed.    Not adding a normalizer.    Data fully loaded into memory.    Initial learning rate is tuned to 100.000000    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 130 | 4 | 0.9701    negative || 8 | 212 | 0.9636    ||======================    Precision || 0.9420 | 0.9815 |    OVERALL 0/1 ACCURACY: 0.966102    LOG LOSS/instance: Infinity    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): -Infinity    AUC: 0.992775    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 96 | 9 | 0.9143    negative || 11 | 213 | 0.9509    ||======================    Precision || 0.8972 | 0.9595 |    OVERALL 0/1 ACCURACY: 0.939210    LOG LOSS/instance: Infinity    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): -Infinity    AUC: 0.963435      OVERALL RESULTS    ---------------------------------------    AUC: 0.978105 (0.0147)    Accuracy: 0.952656 (0.0134)    Positive precision: 0.919613 (0.0224)    Positive recall: 0.942217 (0.0279)    Negative precision: 0.970470 (0.0110)    Negative recall: 0.957265 (0.0064)    Log-loss: Infinity (NaN)    Log-loss reduction: -Infinity (NaN)    F1 Score: 0.930771 (0.0251)    AUPRC: 0.966884 (0.0193)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:08 PM Time elapsed(s): 0.014      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SymSGD/osx-arm64/SymSGD-CV-breast-cancer-out.txt  Output matches baseline: 'SymSGD/SymSGD-CV-breast-cancer-out.txt'  *** Failure #2: Baseline file not found: /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SymSGD/SymSGD-CV-breast-cancer-rp.txt  Void Fail(System.String, System.Object[]) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs 244  Boolean CheckBaseFile(System.String) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs 956  Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs 425  Boolean CheckEqualityNormalized(System.String, System.String, System.String, Int32, NumberParseOption) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs 397  Void Run(RunContext, Int32, NumberParseOption) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestPredictorsMaml.cs 212  Void Run_CV(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestPredictorsMaml.cs 445  Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) /Users/runner/work/1/s/test/Microsoft.ML.TestFramework/BaseTestPredictorsMaml.cs 366  Void BinaryClassifierSymSgdTest() /Users/runner/work/1/s/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs 293  System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0  System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0  System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task b__1() 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0  System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215  Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0  System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0  Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0  System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163  System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0  System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149  Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206  Void MoveNext() 0  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185  Void MoveNext() 0  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0  Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346  Void Start[TStateMachine](TStateMachine ByRef) 0  Void Start[TStateMachine](TStateMachine ByRef) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0  System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250  Void InnerInvoke() 0  Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0  Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0  Boolean Dispatch() 0  Void WorkerThreadStart() 0  Void StartCallback() 0  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SymSGD/SymSGD-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SymSGD/osx-arm64/SymSGD-CV-breast-cancer.txt  Output matches baseline: 'SymSGD/SymSGD-CV-breast-cancer.txt'  Test BinaryClassifierSymSgdTest: completed normally: failed  Test BinaryClassifierSymSgdTest is using osx-arm64 configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBestDiverseSelectorTest with memory usage 136,036,352.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesBestDiverseSelectorTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.MulticlassLRNonNegativeTest  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{nm=20 pt=BestDiverseSelector tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{nm=20 pt=BestDiverseSelector tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 20 learners for the batch 1    Beginning training model 1 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 1 of 20 finished in 00:00:00.0122100    Beginning training model 2 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 2 of 20 finished in 00:00:00.0043006    Beginning training model 3 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 3 of 20 finished in 00:00:00.0026097    Beginning training model 4 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 4 of 20 finished in 00:00:00.0041187    Beginning training model 5 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 5 of 20 finished in 00:00:00.0032698    Beginning training model 6 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 6 of 20 finished in 00:00:00.0013111    Beginning training model 7 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 7 of 20 finished in 00:00:00.0010943    Beginning training model 8 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 8 of 20 finished in 00:00:00.0010682    Beginning training model 9 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)    Trainer 9 of 20 finished in 00:00:00.0011044    Beginning training model 10 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 10 of 20 finished in 00:00:00.0010865    Beginning training model 11 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter)    Trainer 11 of 20 finished in 00:00:00.0010959    Beginning training model 12 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 12 of 20 finished in 00:00:00.0011115    Beginning training model 13 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 13 of 20 finished in 00:00:00.0011020    Beginning training model 14 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 14 of 20 finished in 00:00:00.0024470    Beginning training model 15 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter)    Trainer 15 of 20 finished in 00:00:00.0024336    Beginning training model 16 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter)    Trainer 16 of 20 finished in 00:00:00.0010762    Beginning training model 17 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 17 of 20 finished in 00:00:00.0011175    Beginning training model 18 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 20 instances with missing features during training (over 1 iterations; 20 inst/iter)    Trainer 18 of 20 finished in 00:00:00.0016515    Beginning training model 19 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)    Trainer 19 of 20 finished in 00:00:00.0011482    Beginning training model 20 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 20 of 20 finished in 00:00:00.0011342    Warning:  10 of 20 trainings failed.    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 236 | 3 | 0.9874    negative || 14 | 430 | 0.9685    ||======================    Precision || 0.9440 | 0.9931 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.114893    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.876989    AUC: 0.996127      OVERALL RESULTS    ---------------------------------------    AUC: 0.996127 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.944000 (0.0000)    Positive recall: 0.987448 (0.0000)    Negative precision: 0.993072 (0.0000)    Negative recall: 0.968468 (0.0000)    Log-loss: 0.114893 (0.0000)    Log-loss reduction: 0.876989 (0.0000)    F1 Score: 0.965235 (0.0000)    AUPRC: 0.992160 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:09 PM Time elapsed(s): 0.104      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Diverse-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 236 | 3 | 0.9874    negative || 14 | 430 | 0.9685    ||======================    Precision || 0.9440 | 0.9931 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.114893    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.876989    AUC: 0.996127      OVERALL RESULTS    ---------------------------------------    AUC: 0.996127 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.944000 (0.0000)    Positive recall: 0.987448 (0.0000)    Negative precision: 0.993072 (0.0000)    Negative recall: 0.968468 (0.0000)    Log-loss: 0.114893 (0.0000)    Log-loss reduction: 0.876989 (0.0000)    F1 Score: 0.965235 (0.0000)    AUPRC: 0.992160 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:09 PM Time elapsed(s): 0.023      Suffix of length 34 compared against sequence of length 119  Test EnsemblesBestDiverseSelectorTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.DartLightGBMTest [SKIP]  LightGBM is 64-bit only  Microsoft.ML.RunTests.TestPredictors.RankingTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestIniModels.TestGamRegressionIni with memory usage 136,085,504.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestIniModels.TestGamRegressionIni [PASS]  Output:  Test TestGamRegressionIni: aborted: passed Starting test: Microsoft.ML.RunTests.TestIniModels.TestGamBinaryClassificationIni Finished test: Microsoft.ML.RunTests.TestPredictors.MulticlassLRNonNegativeTest with memory usage 138,199,040.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.MulticlassLRNonNegativeTest [PASS]  Output:  Running 'MulticlassLogisticRegression' on 'iris'  Running as: TrainTest tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt xf=Term{col=Label} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris.txt} norm=no  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris-model.zip seed=1 xf=Term{col=Label}  Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionGaussianNormTest   Not adding a normalizer.    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 48 | 2 | 0.9600    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9600 |0.9600 |    Accuracy(micro-avg): 0.973333    Accuracy(macro-avg): 0.973333    Log-loss: 0.099380    Log-loss reduction: 0.909540      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.973333 (0.0000)    Accuracy(macro-avg): 0.973333 (0.0000)    Log-loss: 0.099380 (0.0000)    Log-loss reduction: 0.909540 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:09 PM Time elapsed(s): 0.061      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris-out.txt  Output matches baseline: 'MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/netcoreapp/LogisticRegression-Non-Negative-TrainTest-iris-rp.txt  Output matches baseline: 'MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/osx-arm64/LogisticRegression-Non-Negative-TrainTest-iris.txt  Output matches baseline: 'MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-TrainTest-iris-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 48 | 2 | 0.9600    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9600 |0.9600 |    Accuracy(micro-avg): 0.973333    Accuracy(macro-avg): 0.973333    Log-loss: 0.099380    Log-loss reduction: 0.909540      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.973333 (0.0000)    Accuracy(macro-avg): 0.973333 (0.0000)    Log-loss: 0.099380 (0.0000)    Log-loss reduction: 0.909540 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:09 PM Time elapsed(s): 0.007      Suffix of length 27 compared against sequence of length 34  Running 'MulticlassLogisticRegression' on 'iris'  Running as: CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 xf=Term{col=Label} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-CV-iris.txt} norm=no threads-  maml.exe CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} threads=- norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-CV-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 xf=Term{col=Label}    Not adding a normalizer.    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 21 | 0 | 0 | 1.0000    1 || 0 | 29 | 1 | 0.9667    2 || 0 | 2 | 26 | 0.9286    ||========================    Precision ||1.0000 |0.9355 |0.9630 |    Accuracy(micro-avg): 0.962025    Accuracy(macro-avg): 0.965079    Log-loss: 0.129885    Log-loss reduction: 0.880567      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 29 | 0 | 0 | 1.0000    1 || 0 | 18 | 2 | 0.9000    2 || 0 | 0 | 22 | 1.0000    ||========================    Precision ||1.0000 |1.0000 |0.9167 |    Accuracy(micro-avg): 0.971831    Accuracy(macro-avg): 0.966667    Log-loss: 0.125565    Log-loss reduction: 0.884341      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.966928 (0.0049)    Accuracy(macro-avg): 0.965873 (0.0008)    Log-loss: 0.127725 (0.0022)    Log-loss reduction: 0.882454 (0.0019)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:09 PM Time elapsed(s): 0.036      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-CV-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/osx-arm64/LogisticRegression-Non-Negative-CV-iris-out.txt  Output matches baseline: 'MulticlassLogisticRegression/LogisticRegression-Non-Negative-CV-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-CV-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/LogisticRegression-Non-Negative-CV-iris-rp.txt  Output matches baseline: 'MulticlassLogisticRegression/LogisticRegression-Non-Negative-CV-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/LogisticRegression-Non-Negative-CV-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/osx-arm64/LogisticRegression-Non-Negative-CV-iris.txt  Output matches baseline: 'MulticlassLogisticRegression/LogisticRegression-Non-Negative-CV-iris.txt'  Test MulticlassLRNonNegativeTest: completed normally: passed  Test MulticlassLRNonNegativeTest is using osx-arm64 configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionGaussianNormTest with memory usage 138,362,880.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionGaussianNormTest [PASS] Starting test: Microsoft.ML.RunTests.TestPredictors.MulticlassTreeFeaturizedLRTest Output:  Running 'LogisticRegression' on 'breast-cancer'  Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt} xf=MeanVarNormalizer{col=Features}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-model.zip seed=1 xf=MeanVarNormalizer{col=Features}    Not adding a normalizer.    Warning:  Skipped 16 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 229 | 10 | 0.9582    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9582 | 0.9775 |    OVERALL 0/1 ACCURACY: 0.970717    LOG LOSS/instance: 0.110699    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.881479    AUC: 0.996231      OVERALL RESULTS    ---------------------------------------    AUC: 0.996231 (0.0000)    Accuracy: 0.970717 (0.0000)    Positive precision: 0.958159 (0.0000)    Positive recall: 0.958159 (0.0000)    Negative precision: 0.977477 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.110699 (0.0000)    Log-loss reduction: 0.881479 (0.0000)    F1 Score: 0.958159 (0.0000)    AUPRC: 0.992209 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:09 PM Time elapsed(s): 0.039      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 229 | 10 | 0.9582    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9582 | 0.9775 |    OVERALL 0/1 ACCURACY: 0.970717    LOG LOSS/instance: 0.110699    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.881479    AUC: 0.996231      OVERALL RESULTS    ---------------------------------------    AUC: 0.996231 (0.0000)    Accuracy: 0.970717 (0.0000)    Positive precision: 0.958159 (0.0000)    Positive recall: 0.958159 (0.0000)    Negative precision: 0.977477 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.110699 (0.0000)    Log-loss reduction: 0.881479 (0.0000)    F1 Score: 0.958159 (0.0000)    AUPRC: 0.992209 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:09 PM Time elapsed(s): 0.008      Suffix of length 34 compared against sequence of length 42  Running 'LogisticRegression' on 'breast-cancer'  Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt} xf=MeanVarNormalizer{col=Features} threads-  maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 xf=MeanVarNormalizer{col=Features}    Not adding a normalizer.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 9 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 129 | 5 | 0.9627    negative || 7 | 213 | 0.9682    ||======================    Precision || 0.9485 | 0.9771 |    OVERALL 0/1 ACCURACY: 0.966102    LOG LOSS/instance: 0.133256    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.860756    AUC: 0.994267    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 96 | 9 | 0.9143    negative || 3 | 221 | 0.9866    ||======================    Precision || 0.9697 | 0.9609 |    OVERALL 0/1 ACCURACY: 0.963526    LOG LOSS/instance: 0.117262    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.870207    AUC: 0.997449      OVERALL RESULTS    ---------------------------------------    AUC: 0.995858 (0.0016)    Accuracy: 0.964814 (0.0013)    Positive precision: 0.959113 (0.0106)    Positive recall: 0.938486 (0.0242)    Negative precision: 0.968967 (0.0081)    Negative recall: 0.977394 (0.0092)    Log-loss: 0.125259 (0.0080)    Log-loss reduction: 0.865481 (0.0047)    F1 Score: 0.948366 (0.0072)    AUPRC: 0.991982 (0.0025)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:09 PM Time elapsed(s): 0.031      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-GaussianNorm-CV-breast-cancer.txt'  Test BinaryClassifierLogisticRegressionGaussianNormTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.FastTreeUnderbuiltRegressionTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestIniModels.TestGamBinaryClassificationIni with memory usage 139,165,696.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestIniModels.TestGamBinaryClassificationIni [PASS]  Output:  Test TestGamBinaryClassificationIni: aborted: passed Finished test: Microsoft.ML.RunTests.TestPredictors.MulticlassTreeFeaturizedLRTest with memory usage 134,217,728.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.MulticlassTreeFeaturizedLRTest [PASS]  Output:  Running 'MulticlassLogisticRegression' on 'iris-tree-featurized' Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierFastRankClassificationTest  Running as: TrainTest tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} xf=TreeFeat{lps=0 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt} norm=no  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-model.zip seed=1 xf=TreeFeat{lps=0 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves}    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 150 instances    Binning and forming Feature objects    Reserved memory for tree learner: 20436 bytes    Starting to train ...    Not training a calibrator because it is not needed.    Not adding a normalizer.    Beginning optimization    num vars: 72    improvement criterion: Mean Improvement    L1 regularization selected 72 of 72 weights.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 49 | 1 | 0.9800    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9608 |0.9796 |    Accuracy(micro-avg): 0.980000    Accuracy(macro-avg): 0.980000    Log-loss: 0.048652    Log-loss reduction: 0.955715      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.980000 (0.0000)    Accuracy(macro-avg): 0.980000 (0.0000)    Log-loss: 0.048652 (0.0000)    Log-loss reduction: 0.955715 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:10 PM Time elapsed(s): 1.471      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-out.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-rp.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 49 | 1 | 0.9800    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9608 |0.9796 |    Accuracy(micro-avg): 0.980000    Accuracy(macro-avg): 0.980000    Log-loss: 0.048652    Log-loss reduction: 0.955715      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.980000 (0.0000)    Accuracy(macro-avg): 0.980000 (0.0000)    Log-loss: 0.048652 (0.0000)    Log-loss reduction: 0.955715 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:10 PM Time elapsed(s): 0.005      Suffix of length 27 compared against sequence of length 41  Running 'MulticlassLogisticRegression' on 'iris-tree-featurized'  Running as: CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} xf=TreeFeat{lps=0 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized.txt} norm=no threads-  maml.exe CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 xf=TreeFeat{lps=0 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves}    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 71 instances    Binning and forming Feature objects    Reserved memory for tree learner: 16380 bytes    Starting to train ...    Not training a calibrator because it is not needed.    Not adding a normalizer.    Beginning optimization    num vars: 39    improvement criterion: Mean Improvement    L1 regularization selected 39 of 39 weights.    Not training a calibrator because it is not needed.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 79 instances    Binning and forming Feature objects    Reserved memory for tree learner: 17472 bytes    Starting to train ...    Not training a calibrator because it is not needed.    Not adding a normalizer.    Beginning optimization    num vars: 54    improvement criterion: Mean Improvement    L1 regularization selected 54 of 54 weights.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 21 | 0 | 0 | 1.0000    1 || 0 | 25 | 5 | 0.8333    2 || 0 | 1 | 27 | 0.9643    ||========================    Precision ||1.0000 |0.9615 |0.8438 |    Accuracy(micro-avg): 0.924051    Accuracy(macro-avg): 0.932540    Log-loss: 0.330649    Log-loss reduction: 0.695959      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 29 | 0 | 0 | 1.0000    1 || 0 | 19 | 1 | 0.9500    2 || 0 | 2 | 20 | 0.9091    ||========================    Precision ||1.0000 |0.9048 |0.9524 |    Accuracy(micro-avg): 0.957746    Accuracy(macro-avg): 0.953030    Log-loss: 0.157832    Log-loss reduction: 0.854619      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.940899 (0.0168)    Accuracy(macro-avg): 0.942785 (0.0102)    Log-loss: 0.244241 (0.0864)    Log-loss reduction: 0.775289 (0.0793)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:10 PM Time elapsed(s): 0.033      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-out.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-rp.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized.txt'  Running 'MulticlassLogisticRegression' on 'iris-tree-featurized-permuted'  Running as: TrainTest tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} xf=TreeFeat{lps=2 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt} norm=no  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-model.zip seed=1 xf=TreeFeat{lps=2 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves}    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 150 instances    Binning and forming Feature objects    Reserved memory for tree learner: 20436 bytes    Starting to train ...    Not training a calibrator because it is not needed.    Not adding a normalizer.    Beginning optimization    num vars: 81    improvement criterion: Mean Improvement    L1 regularization selected 81 of 81 weights.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 49 | 1 | 0.9800    2 || 0 | 3 | 47 | 0.9400    ||========================    Precision ||1.0000 |0.9423 |0.9792 |    Accuracy(micro-avg): 0.973333    Accuracy(macro-avg): 0.973333    Log-loss: 0.052580    Log-loss reduction: 0.952140      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.973333 (0.0000)    Accuracy(macro-avg): 0.973333 (0.0000)    Log-loss: 0.052580 (0.0000)    Log-loss reduction: 0.952140 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.022      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-out.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-rp.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 49 | 1 | 0.9800    2 || 0 | 3 | 47 | 0.9400    ||========================    Precision ||1.0000 |0.9423 |0.9792 |    Accuracy(micro-avg): 0.973333    Accuracy(macro-avg): 0.973333    Log-loss: 0.052580    Log-loss reduction: 0.952140      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.973333 (0.0000)    Accuracy(macro-avg): 0.973333 (0.0000)    Log-loss: 0.052580 (0.0000)    Log-loss reduction: 0.952140 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.004      Suffix of length 27 compared against sequence of length 41  Running 'MulticlassLogisticRegression' on 'iris-tree-featurized-permuted'  Running as: CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} xf=TreeFeat{lps=2 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted.txt} norm=no threads-  maml.exe CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 xf=TreeFeat{lps=2 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves}    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 71 instances    Binning and forming Feature objects    Reserved memory for tree learner: 16380 bytes    Starting to train ...    Not training a calibrator because it is not needed.    Not adding a normalizer.    Beginning optimization    num vars: 45    improvement criterion: Mean Improvement    L1 regularization selected 44 of 45 weights.    Not training a calibrator because it is not needed.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 79 instances    Binning and forming Feature objects    Reserved memory for tree learner: 17472 bytes    Starting to train ...    Not training a calibrator because it is not needed.    Not adding a normalizer.    Beginning optimization    num vars: 48    improvement criterion: Mean Improvement    L1 regularization selected 48 of 48 weights.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 21 | 0 | 0 | 1.0000    1 || 0 | 25 | 5 | 0.8333    2 || 0 | 1 | 27 | 0.9643    ||========================    Precision ||1.0000 |0.9615 |0.8438 |    Accuracy(micro-avg): 0.924051    Accuracy(macro-avg): 0.932540    Log-loss: 0.201590    Log-loss reduction: 0.814633      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 29 | 0 | 0 | 1.0000    1 || 0 | 19 | 1 | 0.9500    2 || 0 | 1 | 21 | 0.9545    ||========================    Precision ||1.0000 |0.9500 |0.9545 |    Accuracy(micro-avg): 0.971831    Accuracy(macro-avg): 0.968182    Log-loss: 0.101915    Log-loss reduction: 0.906125      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.947941 (0.0239)    Accuracy(macro-avg): 0.950361 (0.0178)    Log-loss: 0.151753 (0.0498)    Log-loss reduction: 0.860379 (0.0457)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.032      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-out.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-rp.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted.txt  Output matches baseline: 'MulticlassLogisticRegression/MulticlassLogisticRegression-CV-iris-tree-featurized-permuted.txt'  Test MulticlassTreeFeaturizedLRTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.GossLightGBMTest [SKIP]  LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierFastRankClassificationTest with memory usage 130,547,712.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierFastRankClassificationTest [PASS]  Output:  Running 'FastRank' on 'breast-cancer'  Running as: TrainTest tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-TrainTest-breast-cancer.txt} eval=Binary{pr={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/prcurve-breast-cancer-prcurve.txt }}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} eval=Binary{pr={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/prcurve-breast-cancer-prcurve.txt }} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays  Starting test: Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombinerWithCategoricalSplits   Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Processed 683 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Reserved memory for tree learner: 3852 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 240 | 1 | 0.9959    negative || 13 | 445 | 0.9716    ||======================    Precision || 0.9486 | 0.9978 |    OVERALL 0/1 ACCURACY: 0.979971    LOG LOSS/instance: 0.092572    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.900387    AUC: 0.995370      OVERALL RESULTS    ---------------------------------------    AUC: 0.995370 (0.0000)    Accuracy: 0.979971 (0.0000)    Positive precision: 0.948617 (0.0000)    Positive recall: 0.995851 (0.0000)    Negative precision: 0.997758 (0.0000)    Negative recall: 0.971616 (0.0000)    Log-loss: 0.092572 (0.0000)    Log-loss reduction: 0.900387 (0.0000)    F1 Score: 0.971660 (0.0000)    AUPRC: 0.970606 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.045      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastRank/FastRank-TrainTest-breast-cancer-out.txt  Output matches baseline: 'FastRank/FastRank-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastRank/FastRank-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'FastRank/FastRank-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastRank/FastRank-TrainTest-breast-cancer.txt  Output matches baseline: 'FastRank/FastRank-TrainTest-breast-cancer.txt'  maml.exe Test eval=Binary{pr={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/prcurve-breast-cancer-prcurve.txt }} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 240 | 1 | 0.9959    negative || 13 | 445 | 0.9716    ||======================    Precision || 0.9486 | 0.9978 |    OVERALL 0/1 ACCURACY: 0.979971    LOG LOSS/instance: 0.092572    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.900387    AUC: 0.995370      OVERALL RESULTS    ---------------------------------------    AUC: 0.995370 (0.0000)    Accuracy: 0.979971 (0.0000)    Positive precision: 0.948617 (0.0000)    Positive recall: 0.995851 (0.0000)    Negative precision: 0.997758 (0.0000)    Negative recall: 0.971616 (0.0000)    Log-loss: 0.092572 (0.0000)    Log-loss reduction: 0.900387 (0.0000)    F1 Score: 0.971660 (0.0000)    AUPRC: 0.970606 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.01      Suffix of length 33 compared against sequence of length 45  Running 'FastRank' on 'breast-cancer'  Running as: CV tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-CV-breast-cancer.txt} threads-  maml.exe CV tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  Skipped 8 instances with missing features during training    Processed 329 instances    Binning and forming Feature objects    Reserved memory for tree learner: 3852 bytes    Starting to train ...    Not training a calibrator because it is not needed.    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  Skipped 8 instances with missing features during training    Processed 354 instances    Binning and forming Feature objects    Reserved memory for tree learner: 3816 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 131 | 3 | 0.9776    negative || 10 | 218 | 0.9561    ||======================    Precision || 0.9291 | 0.9864 |    OVERALL 0/1 ACCURACY: 0.964088    LOG LOSS/instance: 0.211336    Test-set entropy (prior Log-Loss/instance): 0.950799    LOG-LOSS REDUCTION (RIG): 0.777728    AUC: 0.983225    TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 98 | 9 | 0.9159    negative || 5 | 225 | 0.9783    ||======================    Precision || 0.9515 | 0.9615 |    OVERALL 0/1 ACCURACY: 0.958457    LOG LOSS/instance: 0.137700    Test-set entropy (prior Log-Loss/instance): 0.901650    LOG-LOSS REDUCTION (RIG): 0.847280    AUC: 0.993681      OVERALL RESULTS    ---------------------------------------    AUC: 0.988453 (0.0052)    Accuracy: 0.961273 (0.0028)    Positive precision: 0.940267 (0.0112)    Positive recall: 0.946750 (0.0309)    Negative precision: 0.973982 (0.0124)    Negative recall: 0.967201 (0.0111)    Log-loss: 0.174518 (0.0368)    Log-loss reduction: 0.812504 (0.0348)    F1 Score: 0.943030 (0.0097)    AUPRC: 0.962986 (0.0211)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.043      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastRank/FastRank-CV-breast-cancer-out.txt  Output matches baseline: 'FastRank/FastRank-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastRank/FastRank-CV-breast-cancer-rp.txt  Output matches baseline: 'FastRank/FastRank-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastRank/FastRank-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastRank/FastRank-CV-breast-cancer.txt  Output matches baseline: 'FastRank/FastRank-CV-breast-cancer.txt'  Test BinaryClassifierFastRankClassificationTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.RegressorFastRankTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestPredictors.MulticlassifierLightGBMKeyLabelU404Test [SKIP]  LightGBM is 64-bit only  Microsoft.ML.RunTests.TestPredictors.RegressorOgdTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombinerWithCategoricalSplits with memory usage 130,973,696.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombinerWithCategoricalSplits [PASS]  Output:  Test TestTreeEnsembleCombinerWithCategoricalSplits: aborted: passed Starting test: Microsoft.ML.RunTests.TestPredictors.LinearClassifierTest Finished test: Microsoft.ML.RunTests.TestPredictors.LinearClassifierTest with memory usage 133,906,432.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.LinearClassifierTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.DefaultCalibratorPerceptronTest  Running 'SDCA' on 'breast-cancer'  Running as: TrainTest tr=SDCA{maxIterations=5 checkFreq=9 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=SDCA{maxIterations=5 checkFreq=9 nt=1} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Using 1 thread to train.    Warning:  Skipped 16 instances with missing features/label during training    Auto-tuning parameters: L2 = 0.014641289.    Auto-tuning parameters: L1Threshold (L1/L2) = 0.    Using model from last iteration.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 222 | 17 | 0.9289    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9610 | 0.9624 |    OVERALL 0/1 ACCURACY: 0.961933    LOG LOSS/instance: 0.294964    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.684194    AUC: 0.995458      OVERALL RESULTS    ---------------------------------------    AUC: 0.995458 (0.0000)    Accuracy: 0.961933 (0.0000)    Positive precision: 0.961039 (0.0000)    Positive recall: 0.928870 (0.0000)    Negative precision: 0.962389 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.294964 (0.0000)    Log-loss reduction: 0.684194 (0.0000)    F1 Score: 0.944681 (0.0000)    AUPRC: 0.990716 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.022      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-TrainTest-breast-cancer-out.txt  Output matches baseline: 'SDCA/BinarySDCA-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'SDCA/BinarySDCA-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-TrainTest-breast-cancer.txt  Output matches baseline: 'SDCA/BinarySDCA-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 222 | 17 | 0.9289    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9610 | 0.9624 |    OVERALL 0/1 ACCURACY: 0.961933    LOG LOSS/instance: 0.294964    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.684194    AUC: 0.995458      OVERALL RESULTS    ---------------------------------------    AUC: 0.995458 (0.0000)    Accuracy: 0.961933 (0.0000)    Positive precision: 0.961039 (0.0000)    Positive recall: 0.928870 (0.0000)    Negative precision: 0.962389 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.294964 (0.0000)    Log-loss reduction: 0.684194 (0.0000)    F1 Score: 0.944681 (0.0000)    AUPRC: 0.990716 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.006      Suffix of length 34 compared against sequence of length 42  Running 'SDCA' on 'breast-cancer'  Running as: CV tr=SDCA{maxIterations=5 checkFreq=9 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-CV-breast-cancer.txt} threads-  maml.exe CV tr=SDCA{maxIterations=5 checkFreq=9 nt=1} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Using 1 thread to train.    Warning:  Skipped 8 instances with missing features/label during training    Auto-tuning parameters: L2 = 0.030395137.    Auto-tuning parameters: L1Threshold (L1/L2) = 0.    Using model from last iteration.    Not training a calibrator because it is not needed.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Using 1 thread to train.    Warning:  Skipped 8 instances with missing features/label during training    Auto-tuning parameters: L2 = 0.028248588.    Auto-tuning parameters: L1Threshold (L1/L2) = 0.    Using model from last iteration.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 125 | 9 | 0.9328    negative || 7 | 213 | 0.9682    ||======================    Precision || 0.9470 | 0.9595 |    OVERALL 0/1 ACCURACY: 0.954802    LOG LOSS/instance: 0.401674    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.580277    AUC: 0.993284    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 94 | 11 | 0.8952    negative || 2 | 222 | 0.9911    ||======================    Precision || 0.9792 | 0.9528 |    OVERALL 0/1 ACCURACY: 0.960486    LOG LOSS/instance: 0.390543    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.567722    AUC: 0.997321      OVERALL RESULTS    ---------------------------------------    AUC: 0.995303 (0.0020)    Accuracy: 0.957644 (0.0028)    Positive precision: 0.963068 (0.0161)    Positive recall: 0.914037 (0.0188)    Negative precision: 0.956125 (0.0033)    Negative recall: 0.979627 (0.0114)    Log-loss: 0.396109 (0.0056)    Log-loss reduction: 0.573999 (0.0063)    F1 Score: 0.937587 (0.0023)    AUPRC: 0.990827 (0.0033)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.016      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-CV-breast-cancer-out.txt  Output matches baseline: 'SDCA/BinarySDCA-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-CV-breast-cancer-rp.txt  Output matches baseline: 'SDCA/BinarySDCA-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-CV-breast-cancer.txt  Output matches baseline: 'SDCA/BinarySDCA-CV-breast-cancer.txt'  Running 'SDCA' on 'breast-cancer'  Running as: TrainTest tr=SDCA{l2=1e-06 l1=0.5 maxIterations=5 checkFreq=9 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=SDCA{l2=1e-06 l1=0.5 maxIterations=5 checkFreq=9 nt=1} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Using 1 thread to train.    Warning:  Skipped 16 instances with missing features/label during training    Using model from last iteration.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 220 | 19 | 0.9205    negative || 6 | 438 | 0.9865    ||======================    Precision || 0.9735 | 0.9584 |    OVERALL 0/1 ACCURACY: 0.963397    LOG LOSS/instance: 0.135137    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.855314    AUC: 0.996042      OVERALL RESULTS    ---------------------------------------    AUC: 0.996042 (0.0000)    Accuracy: 0.963397 (0.0000)    Positive precision: 0.973451 (0.0000)    Positive recall: 0.920502 (0.0000)    Negative precision: 0.958425 (0.0000)    Negative recall: 0.986486 (0.0000)    Log-loss: 0.135137 (0.0000)    Log-loss reduction: 0.855314 (0.0000)    F1 Score: 0.946237 (0.0000)    AUPRC: 0.991947 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.018      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-L1-TrainTest-breast-cancer-out.txt  Output matches baseline: 'SDCA/BinarySDCA-L1-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-L1-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'SDCA/BinarySDCA-L1-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt  Output matches baseline: 'SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 220 | 19 | 0.9205    negative || 6 | 438 | 0.9865    ||======================    Precision || 0.9735 | 0.9584 |    OVERALL 0/1 ACCURACY: 0.963397    LOG LOSS/instance: 0.135137    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.855314    AUC: 0.996042      OVERALL RESULTS    ---------------------------------------    AUC: 0.996042 (0.0000)    Accuracy: 0.963397 (0.0000)    Positive precision: 0.973451 (0.0000)    Positive recall: 0.920502 (0.0000)    Negative precision: 0.958425 (0.0000)    Negative recall: 0.986486 (0.0000)    Log-loss: 0.135137 (0.0000)    Log-loss reduction: 0.855314 (0.0000)    F1 Score: 0.946237 (0.0000)    AUPRC: 0.991947 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.008      Suffix of length 34 compared against sequence of length 40  Running 'SDCA' on 'breast-cancer'  Running as: CV tr=SDCA{l2=1e-06 l1=0.5 maxIterations=5 checkFreq=9 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-CV-breast-cancer.txt} threads-  maml.exe CV tr=SDCA{l2=1e-06 l1=0.5 maxIterations=5 checkFreq=9 nt=1} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Using 1 thread to train.    Warning:  Skipped 8 instances with missing features/label during training    Using model from last iteration.    Not training a calibrator because it is not needed.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Using 1 thread to train.    Warning:  Skipped 8 instances with missing features/label during training    Using model from last iteration.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 131 | 3 | 0.9776    negative || 8 | 212 | 0.9636    ||======================    Precision || 0.9424 | 0.9860 |    OVERALL 0/1 ACCURACY: 0.968927    LOG LOSS/instance: 0.142232    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.851377    AUC: 0.993860    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 96 | 9 | 0.9143    negative || 2 | 222 | 0.9911    ||======================    Precision || 0.9796 | 0.9610 |    OVERALL 0/1 ACCURACY: 0.966565    LOG LOSS/instance: 0.118621    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.868703    AUC: 0.997449      OVERALL RESULTS    ---------------------------------------    AUC: 0.995655 (0.0018)    Accuracy: 0.967746 (0.0012)    Positive precision: 0.961019 (0.0186)    Positive recall: 0.945949 (0.0317)    Negative precision: 0.973543 (0.0125)    Negative recall: 0.977354 (0.0137)    Log-loss: 0.130426 (0.0118)    Log-loss reduction: 0.860040 (0.0087)    F1 Score: 0.952760 (0.0069)    AUPRC: 0.991454 (0.0030)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.019      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-L1-CV-breast-cancer-out.txt  Output matches baseline: 'SDCA/BinarySDCA-L1-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-L1-CV-breast-cancer-rp.txt  Output matches baseline: 'SDCA/BinarySDCA-L1-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-L1-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-L1-CV-breast-cancer.txt  Output matches baseline: 'SDCA/BinarySDCA-L1-CV-breast-cancer.txt'  Running 'SDCA' on 'breast-cancer'  Running as: TrainTest tr=SDCA{l2=1e-06 loss=SmoothedHinge l1=0.5 maxIterations=5 checkFreq=9 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=SDCA{l2=1e-06 loss=SmoothedHinge l1=0.5 maxIterations=5 checkFreq=9 nt=1} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Using 1 thread to train.    Warning:  Skipped 16 instances with missing features/label during training    Using model from last iteration.    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 224 | 15 | 0.9372    negative || 8 | 436 | 0.9820    ||======================    Precision || 0.9655 | 0.9667 |    OVERALL 0/1 ACCURACY: 0.966325    LOG LOSS/instance: 0.118542    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.873081    AUC: 0.995816      OVERALL RESULTS    ---------------------------------------    AUC: 0.995816 (0.0000)    Accuracy: 0.966325 (0.0000)    Positive precision: 0.965517 (0.0000)    Positive recall: 0.937238 (0.0000)    Negative precision: 0.966741 (0.0000)    Negative recall: 0.981982 (0.0000)    Log-loss: 0.118542 (0.0000)    Log-loss reduction: 0.873081 (0.0000)    F1 Score: 0.951168 (0.0000)    AUPRC: 0.991045 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.016      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-out.txt  Output matches baseline: 'SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt  Output matches baseline: 'SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 224 | 15 | 0.9372    negative || 8 | 436 | 0.9820    ||======================    Precision || 0.9655 | 0.9667 |    OVERALL 0/1 ACCURACY: 0.966325    LOG LOSS/instance: 0.118542    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.873081    AUC: 0.995816      OVERALL RESULTS    ---------------------------------------    AUC: 0.995816 (0.0000)    Accuracy: 0.966325 (0.0000)    Positive precision: 0.965517 (0.0000)    Positive recall: 0.937238 (0.0000)    Negative precision: 0.966741 (0.0000)    Negative recall: 0.981982 (0.0000)    Log-loss: 0.118542 (0.0000)    Log-loss reduction: 0.873081 (0.0000)    F1 Score: 0.951168 (0.0000)    AUPRC: 0.991045 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.006      Suffix of length 34 compared against sequence of length 40  Running 'SDCA' on 'breast-cancer'  Running as: CV tr=SDCA{l2=1e-06 loss=SmoothedHinge l1=0.5 maxIterations=5 checkFreq=9 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt} threads-  maml.exe CV tr=SDCA{l2=1e-06 loss=SmoothedHinge l1=0.5 maxIterations=5 checkFreq=9 nt=1} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Using 1 thread to train.    Warning:  Skipped 8 instances with missing features/label during training    Using model from last iteration.    Training calibrator.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Using 1 thread to train.    Warning:  Skipped 8 instances with missing features/label during training    Using model from last iteration.    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 130 | 4 | 0.9701    negative || 8 | 212 | 0.9636    ||======================    Precision || 0.9420 | 0.9815 |    OVERALL 0/1 ACCURACY: 0.966102    LOG LOSS/instance: 0.129889    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.864274    AUC: 0.994539    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 38 | 67 | 0.3619    negative || 0 | 224 | 1.0000    ||======================    Precision || 1.0000 | 0.7698 |    OVERALL 0/1 ACCURACY: 0.796353    LOG LOSS/instance: 0.126797    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.859653    AUC: 0.996854      OVERALL RESULTS    ---------------------------------------    AUC: 0.995696 (0.0012)    Accuracy: 0.881227 (0.0849)    Positive precision: 0.971014 (0.0290)    Positive recall: 0.666027 (0.3041)    Negative precision: 0.875620 (0.1059)    Negative recall: 0.981818 (0.0182)    Log-loss: 0.128343 (0.0015)    Log-loss reduction: 0.861964 (0.0023)    F1 Score: 0.743675 (0.2122)    AUPRC: 0.991479 (0.0016)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.018      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer-out.txt  Output matches baseline: 'SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer-rp.txt  Output matches baseline: 'SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt  Output matches baseline: 'SDCA/BinarySDCA-SmoothedHinge-CV-breast-cancer.txt'  Running 'SGD' on 'breast-cancer'  Running as: TrainTest tr=SGD{maxIterations=2 checkFreq=9 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=SGD{maxIterations=2 checkFreq=9 nt=1} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 16 instances with missing features during training    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 225 | 14 | 0.9414    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9615 | 0.9688 |    OVERALL 0/1 ACCURACY: 0.966325    LOG LOSS/instance: 0.494040    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.471051    AUC: 0.995156      OVERALL RESULTS    ---------------------------------------    AUC: 0.995156 (0.0000)    Accuracy: 0.966325 (0.0000)    Positive precision: 0.961538 (0.0000)    Positive recall: 0.941423 (0.0000)    Negative precision: 0.968820 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.494040 (0.0000)    Log-loss reduction: 0.471051 (0.0000)    F1 Score: 0.951374 (0.0000)    AUPRC: 0.990094 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.013      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-TrainTest-breast-cancer-out.txt  Output matches baseline: 'SGD/BinarySGD-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'SGD/BinarySGD-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-TrainTest-breast-cancer.txt  Output matches baseline: 'SGD/BinarySGD-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 225 | 14 | 0.9414    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9615 | 0.9688 |    OVERALL 0/1 ACCURACY: 0.966325    LOG LOSS/instance: 0.494040    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.471051    AUC: 0.995156      OVERALL RESULTS    ---------------------------------------    AUC: 0.995156 (0.0000)    Accuracy: 0.966325 (0.0000)    Positive precision: 0.961538 (0.0000)    Positive recall: 0.941423 (0.0000)    Negative precision: 0.968820 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.494040 (0.0000)    Log-loss reduction: 0.471051 (0.0000)    F1 Score: 0.951374 (0.0000)    AUPRC: 0.990094 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.006      Suffix of length 34 compared against sequence of length 38  Running 'SGD' on 'breast-cancer'  Running as: CV tr=SGD{maxIterations=2 checkFreq=9 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-CV-breast-cancer.txt} threads-  maml.exe CV tr=SGD{maxIterations=2 checkFreq=9 nt=1} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 8 instances with missing features during training    Not training a calibrator because it is not needed.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 8 instances with missing features during training    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 124 | 10 | 0.9254    negative || 6 | 214 | 0.9727    ||======================    Precision || 0.9538 | 0.9554 |    OVERALL 0/1 ACCURACY: 0.954802    LOG LOSS/instance: 0.670855    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.299001    AUC: 0.993046    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 97 | 8 | 0.9238    negative || 2 | 222 | 0.9911    ||======================    Precision || 0.9798 | 0.9652 |    OVERALL 0/1 ACCURACY: 0.969605    LOG LOSS/instance: 0.657158    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.272615    AUC: 0.997066      OVERALL RESULTS    ---------------------------------------    AUC: 0.995056 (0.0020)    Accuracy: 0.962204 (0.0074)    Positive precision: 0.966822 (0.0130)    Positive recall: 0.924591 (0.0008)    Negative precision: 0.960287 (0.0049)    Negative recall: 0.981899 (0.0092)    Log-loss: 0.664007 (0.0068)    Log-loss reduction: 0.285808 (0.0132)    F1 Score: 0.945187 (0.0058)    AUPRC: 0.990233 (0.0032)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.016      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-CV-breast-cancer-out.txt  Output matches baseline: 'SGD/BinarySGD-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-CV-breast-cancer-rp.txt  Output matches baseline: 'SGD/BinarySGD-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-CV-breast-cancer.txt  Output matches baseline: 'SGD/BinarySGD-CV-breast-cancer.txt'  Running 'SGD' on 'breast-cancer'  Running as: TrainTest tr=SGD{loss=Hinge maxIterations=2 checkFreq=9 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=SGD{loss=Hinge maxIterations=2 checkFreq=9 nt=1} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 16 instances with missing features during training    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 228 | 11 | 0.9540    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9580 | 0.9753 |    OVERALL 0/1 ACCURACY: 0.969253    LOG LOSS/instance: 0.129716    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.861119    AUC: 0.995307      OVERALL RESULTS    ---------------------------------------    AUC: 0.995307 (0.0000)    Accuracy: 0.969253 (0.0000)    Positive precision: 0.957983 (0.0000)    Positive recall: 0.953975 (0.0000)    Negative precision: 0.975281 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.129716 (0.0000)    Log-loss reduction: 0.861119 (0.0000)    F1 Score: 0.955975 (0.0000)    AUPRC: 0.990403 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.012      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-Hinge-TrainTest-breast-cancer-out.txt  Output matches baseline: 'SGD/BinarySGD-Hinge-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-Hinge-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'SGD/BinarySGD-Hinge-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt  Output matches baseline: 'SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 228 | 11 | 0.9540    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9580 | 0.9753 |    OVERALL 0/1 ACCURACY: 0.969253    LOG LOSS/instance: 0.129716    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.861119    AUC: 0.995307      OVERALL RESULTS    ---------------------------------------    AUC: 0.995307 (0.0000)    Accuracy: 0.969253 (0.0000)    Positive precision: 0.957983 (0.0000)    Positive recall: 0.953975 (0.0000)    Negative precision: 0.975281 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.129716 (0.0000)    Log-loss reduction: 0.861119 (0.0000)    F1 Score: 0.955975 (0.0000)    AUPRC: 0.990403 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.006      Suffix of length 34 compared against sequence of length 38  Running 'SGD' on 'breast-cancer'  Running as: CV tr=SGD{loss=Hinge maxIterations=2 checkFreq=9 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-CV-breast-cancer.txt} threads-  maml.exe CV tr=SGD{loss=Hinge maxIterations=2 checkFreq=9 nt=1} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 8 instances with missing features during training    Training calibrator.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 8 instances with missing features during training    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 125 | 9 | 0.9328    negative || 7 | 213 | 0.9682    ||======================    Precision || 0.9470 | 0.9595 |    OVERALL 0/1 ACCURACY: 0.954802    LOG LOSS/instance: 0.150656    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.842575    AUC: 0.993114    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 94 | 11 | 0.8952    negative || 2 | 222 | 0.9911    ||======================    Precision || 0.9792 | 0.9528 |    OVERALL 0/1 ACCURACY: 0.960486    LOG LOSS/instance: 0.127422    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.858961    AUC: 0.997109      OVERALL RESULTS    ---------------------------------------    AUC: 0.995111 (0.0020)    Accuracy: 0.957644 (0.0028)    Positive precision: 0.963068 (0.0161)    Positive recall: 0.914037 (0.0188)    Negative precision: 0.956125 (0.0033)    Negative recall: 0.979627 (0.0114)    Log-loss: 0.139039 (0.0116)    Log-loss reduction: 0.850768 (0.0082)    F1 Score: 0.937587 (0.0023)    AUPRC: 0.990374 (0.0032)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.014      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-Hinge-CV-breast-cancer-out.txt  Output matches baseline: 'SGD/BinarySGD-Hinge-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-Hinge-CV-breast-cancer-rp.txt  Output matches baseline: 'SGD/BinarySGD-Hinge-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/SGD/BinarySGD-Hinge-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/SGD/BinarySGD-Hinge-CV-breast-cancer.txt  Output matches baseline: 'SGD/BinarySGD-Hinge-CV-breast-cancer.txt'  Test LinearClassifierTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.RankingLightGBMTest [SKIP]  Need to find ranking dataset.  Microsoft.ML.RunTests.TestPredictors.GamRegressionTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestPredictors.OneClassSvmLibsvmWrapperDenseTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestPredictors.RegressorLightGBMRMSETest [SKIP]  LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.DefaultCalibratorPerceptronTest with memory usage 134,119,424.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.DefaultCalibratorPerceptronTest [PASS]  Output:  Running 'AveragedPerceptron' on 'breast-cancer'  Running as: TrainTest tr=AveragedPerceptron data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt} cali={}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=AveragedPerceptron cali={} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter)  Starting test: Microsoft.ML.RunTests.TestPredictors.FastForestRegressionTest   Not training a calibrator because a valid calibrator trainer was not provided.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 234 | 5 | 0.9791    negative || 12 | 432 | 0.9730    ||======================    Precision || 0.9512 | 0.9886 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: NaN    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.000000    AUC: 0.996146      OVERALL RESULTS    ---------------------------------------    AUC: 0.996146 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.951220 (0.0000)    Positive recall: 0.979079 (0.0000)    Negative precision: 0.988558 (0.0000)    Negative recall: 0.972973 (0.0000)    Log-loss: NaN (0.0000)    Log-loss reduction: 0.000000 (0.0000)    F1 Score: 0.964948 (0.0000)    AUPRC: 0.992065 (0.0000)      ---------------------------------------    Warning:  Data does not contain a probability column. Will not output the Log-loss column    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.013      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.nocalibration-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 234 | 5 | 0.9791    negative || 12 | 432 | 0.9730    ||======================    Precision || 0.9512 | 0.9886 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: NaN    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.000000    AUC: 0.996146      OVERALL RESULTS    ---------------------------------------    AUC: 0.996146 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.951220 (0.0000)    Positive recall: 0.979079 (0.0000)    Negative precision: 0.988558 (0.0000)    Negative recall: 0.972973 (0.0000)    Log-loss: NaN (0.0000)    Log-loss reduction: 0.000000 (0.0000)    F1 Score: 0.964948 (0.0000)    AUPRC: 0.992065 (0.0000)      ---------------------------------------    Warning:  Data does not contain a probability column. Will not output the Log-loss column    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.005      Suffix of length 35 compared against sequence of length 39  Running 'AveragedPerceptron' on 'breast-cancer'  Running as: CV tr=AveragedPerceptron data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt} threads- cali={}  maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)    Not training a calibrator because a valid calibrator trainer was not provided.    Warning:  Data does not contain a probability column. Will not output the Log-loss column    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)    Not training a calibrator because a valid calibrator trainer was not provided.    Warning:  Data does not contain a probability column. Will not output the Log-loss column    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 133 | 1 | 0.9925    negative || 9 | 211 | 0.9591    ||======================    Precision || 0.9366 | 0.9953 |    OVERALL 0/1 ACCURACY: 0.971751    LOG LOSS/instance: NaN    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.000000    AUC: 0.994403    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 100 | 5 | 0.9524    negative || 3 | 221 | 0.9866    ||======================    Precision || 0.9709 | 0.9779 |    OVERALL 0/1 ACCURACY: 0.975684    LOG LOSS/instance: NaN    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.000000    AUC: 0.997619      OVERALL RESULTS    ---------------------------------------    AUC: 0.996011 (0.0016)    Accuracy: 0.973718 (0.0020)    Positive precision: 0.953747 (0.0171)    Positive recall: 0.972459 (0.0201)    Negative precision: 0.986580 (0.0087)    Negative recall: 0.972849 (0.0138)    Log-loss: NaN (NaN)    Log-loss reduction: 0.000000 (0.0000)    F1 Score: 0.962653 (0.0011)    AUPRC: 0.992269 (0.0025)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.016      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.nocalibration.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.nocalibration.txt'  Test DefaultCalibratorPerceptronTest: completed normally: passed  Test DefaultCalibratorPerceptronTest is using osx-arm64 configuration specific baselines.  Microsoft.ML.RunTests.TestPredictors.PoissonRegressorTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestPredictors.EarlyStoppingTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestPredictors.PcaAnomalyTest [SKIP]  Test flaky. Disabling until resolved.  Microsoft.ML.RunTests.TestPredictors.WeightingRankingPredictorsTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.FastForestRegressionTest with memory usage 134,545,408.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.FastForestRegressionTest [PASS]  Output:  Running 'FastForestRegression' on 'housing'  Running as: TrainTest tr=FastForestRegression{nl=5 mil=5 iter=20} data=/tmp/helix/working/99790869/p/test/data/housing.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/housing.txt loader=Text{col=Label:0 col=Features:~ header=+} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-TrainTest-housing-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-TrainTest-housing.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/housing.txt tr=FastForestRegression{nl=5 mil=5 iter=20} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-TrainTest-housing.txt loader=Text{col=Label:0 col=Features:~ header=+} data=/tmp/helix/working/99790869/p/test/data/housing.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-TrainTest-housing-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays  Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierTesterThresholdingTest   Changing data from row-wise to column-wise    Processed 506 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Reserved memory for tree learner: 67032 bytes    Starting to train ...    Not training a calibrator because it is not needed.    L1(avg): 2.970635    L2(avg): 17.744904    RMS(avg): 4.212470    Loss-fn(avg): 17.744904    R Squared: 0.789801      OVERALL RESULTS    ---------------------------------------    L1(avg): 2.970635 (0.0000)    L2(avg): 17.744904 (0.0000)    RMS(avg): 4.212470 (0.0000)    Loss-fn(avg): 17.744904 (0.0000)    R Squared: 0.789801 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.056      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-TrainTest-housing-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/FastForestRegression-TrainTest-housing-out.txt  Output matches baseline: 'FastForestRegression/FastForestRegression-TrainTest-housing-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-TrainTest-housing-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/FastForestRegression-TrainTest-housing-rp.txt  Output matches baseline: 'FastForestRegression/FastForestRegression-TrainTest-housing-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-TrainTest-housing.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/FastForestRegression-TrainTest-housing.txt  Output matches baseline: 'FastForestRegression/FastForestRegression-TrainTest-housing.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-TrainTest-housing.txt data=/tmp/helix/working/99790869/p/test/data/housing.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-TrainTest-housing-model.zip seed=1    L1(avg): 2.970635    L2(avg): 17.744904    RMS(avg): 4.212470    Loss-fn(avg): 17.744904    R Squared: 0.789801      OVERALL RESULTS    ---------------------------------------    L1(avg): 2.970635 (0.0000)    L2(avg): 17.744904 (0.0000)    RMS(avg): 4.212470 (0.0000)    Loss-fn(avg): 17.744904 (0.0000)    R Squared: 0.789801 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.006      Suffix of length 19 compared against sequence of length 29  Running 'FastForestRegression' on 'housing'  Running as: CV tr=FastForestRegression{nl=5 mil=5 iter=20} data=/tmp/helix/working/99790869/p/test/data/housing.txt seed=1 loader=Text{col=Label:0 col=Features:~ header=+} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-CV-housing.txt} threads-  maml.exe CV tr=FastForestRegression{nl=5 mil=5 iter=20} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-CV-housing.txt loader=Text{col=Label:0 col=Features:~ header=+} data=/tmp/helix/working/99790869/p/test/data/housing.txt seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 241 instances    Binning and forming Feature objects    Reserved memory for tree learner: 56772 bytes    Starting to train ...    Not training a calibrator because it is not needed.    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 265 instances    Binning and forming Feature objects    Reserved memory for tree learner: 60444 bytes    Starting to train ...    Not training a calibrator because it is not needed.    L1(avg): 3.297530    L2(avg): 22.053650    RMS(avg): 4.696131    Loss-fn(avg): 22.053650    R Squared: 0.736495    L1(avg): 3.262365    L2(avg): 19.891088    RMS(avg): 4.459943    Loss-fn(avg): 19.891088    R Squared: 0.766574      OVERALL RESULTS    ---------------------------------------    L1(avg): 3.279948 (0.0176)    L2(avg): 20.972369 (1.0813)    RMS(avg): 4.578037 (0.1181)    Loss-fn(avg): 20.972369 (1.0813)    R Squared: 0.751534 (0.0150)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:11 PM Time elapsed(s): 0.058      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-CV-housing-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/FastForestRegression-CV-housing-out.txt  Output matches baseline: 'FastForestRegression/FastForestRegression-CV-housing-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-CV-housing-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/FastForestRegression-CV-housing-rp.txt  Output matches baseline: 'FastForestRegression/FastForestRegression-CV-housing-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/FastForestRegression-CV-housing.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/FastForestRegression-CV-housing.txt  Output matches baseline: 'FastForestRegression/FastForestRegression-CV-housing.txt'  Running 'FastForestRegression' on 'housing'  Running as: TrainTest tr=FastForestRegression{nl=5 mil=5 iter=20} data=/tmp/helix/working/99790869/p/test/data/housing.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/housing.txt loader=Text{col=Label:0 col=Features:~ header=+} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-TrainTest-housing-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-TrainTest-housing.txt} scorer=QuantileRegression{quantiles = 0.25,0.5,0.75}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/housing.txt tr=FastForestRegression{nl=5 mil=5 iter=20} scorer=QuantileRegression{quantiles = 0.25,0.5,0.75} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-TrainTest-housing.txt loader=Text{col=Label:0 col=Features:~ header=+} data=/tmp/helix/working/99790869/p/test/data/housing.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-TrainTest-housing-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 506 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Reserved memory for tree learner: 67032 bytes    Starting to train ...    Not training a calibrator because it is not needed.    L1(avg): 2.840810    L2(avg): 17.723068    RMS(avg): 4.209877    Loss-fn(avg): 17.723068    R Squared: 0.790060      OVERALL RESULTS    ---------------------------------------    L1(avg): 2.840810 (0.0000)    L2(avg): 17.723068 (0.0000)    RMS(avg): 4.209877 (0.0000)    Loss-fn(avg): 17.723068 (0.0000)    R Squared: 0.790060 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.221      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-TrainTest-housing-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/QuantileRegressorTester-TrainTest-housing-out.txt  Output matches baseline: 'FastForestRegression/QuantileRegressorTester-TrainTest-housing-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-TrainTest-housing-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/QuantileRegressorTester-TrainTest-housing-rp.txt  Output matches baseline: 'FastForestRegression/QuantileRegressorTester-TrainTest-housing-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-TrainTest-housing.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/QuantileRegressorTester-TrainTest-housing.txt  Output matches baseline: 'FastForestRegression/QuantileRegressorTester-TrainTest-housing.txt'  maml.exe Test scorer=QuantileRegression{quantiles = 0.25,0.5,0.75} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-TrainTest-housing.txt data=/tmp/helix/working/99790869/p/test/data/housing.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-TrainTest-housing-model.zip seed=1    L1(avg): 2.840810    L2(avg): 17.723068    RMS(avg): 4.209877    Loss-fn(avg): 17.723068    R Squared: 0.790060      OVERALL RESULTS    ---------------------------------------    L1(avg): 2.840810 (0.0000)    L2(avg): 17.723068 (0.0000)    RMS(avg): 4.209877 (0.0000)    Loss-fn(avg): 17.723068 (0.0000)    R Squared: 0.790060 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.151      Suffix of length 19 compared against sequence of length 29  Running 'FastForestRegression' on 'housing'  Running as: CV tr=FastForestRegression{nl=5 mil=5 iter=20} data=/tmp/helix/working/99790869/p/test/data/housing.txt seed=1 loader=Text{col=Label:0 col=Features:~ header=+} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-CV-housing.txt} threads- scorer=QuantileRegression{quantiles = 0.25,0.5,0.75}  maml.exe CV tr=FastForestRegression{nl=5 mil=5 iter=20} scorer=QuantileRegression{quantiles = 0.25,0.5,0.75} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-CV-housing.txt loader=Text{col=Label:0 col=Features:~ header=+} data=/tmp/helix/working/99790869/p/test/data/housing.txt seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 241 instances    Binning and forming Feature objects    Reserved memory for tree learner: 56772 bytes    Starting to train ...    Not training a calibrator because it is not needed.    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 265 instances    Binning and forming Feature objects    Reserved memory for tree learner: 60444 bytes    Starting to train ...    Not training a calibrator because it is not needed.    L1(avg): 3.180943    L2(avg): 22.529859    RMS(avg): 4.746563    Loss-fn(avg): 22.529858    R Squared: 0.730805    L1(avg): 3.240456    L2(avg): 20.642272    RMS(avg): 4.543377    Loss-fn(avg): 20.642272    R Squared: 0.757759      OVERALL RESULTS    ---------------------------------------    L1(avg): 3.210700 (0.0298)    L2(avg): 21.586065 (0.9438)    RMS(avg): 4.644970 (0.1016)    Loss-fn(avg): 21.586065 (0.9438)    R Squared: 0.744282 (0.0135)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.254      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-CV-housing-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/QuantileRegressorTester-CV-housing-out.txt  Output matches baseline: 'FastForestRegression/QuantileRegressorTester-CV-housing-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-CV-housing-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/QuantileRegressorTester-CV-housing-rp.txt  Output matches baseline: 'FastForestRegression/QuantileRegressorTester-CV-housing-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestRegression/QuantileRegressorTester-CV-housing.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestRegression/QuantileRegressorTester-CV-housing.txt  Output matches baseline: 'FastForestRegression/QuantileRegressorTester-CV-housing.txt'  Test FastForestRegressionTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.WeightingClassificationLRPredictorsTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierTesterThresholdingTest with memory usage 134,791,168.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierTesterThresholdingTest [PASS]  Output:  Running 'LogisticRegression' on 'breast-cancer' Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerTest  Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt} norm=no eval=BinaryClassifier{threshold=0.95 useRawScore=-}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold-model.zip seed=1    Not adding a normalizer.    Warning:  Skipped 16 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 198 | 41 | 0.8285    negative || 3 | 441 | 0.9932    ||======================    Precision || 0.9851 | 0.9149 |    OVERALL 0/1 ACCURACY: 0.935578    LOG LOSS/instance: 0.111003    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.881154    AUC: 0.996136      OVERALL RESULTS    ---------------------------------------    AUC: 0.996136 (0.0000)    Accuracy: 0.935578 (0.0000)    Positive precision: 0.985075 (0.0000)    Positive recall: 0.828452 (0.0000)    Negative precision: 0.914938 (0.0000)    Negative recall: 0.993243 (0.0000)    Log-loss: 0.111003 (0.0000)    Log-loss reduction: 0.881154 (0.0000)    F1 Score: 0.900000 (0.0000)    AUPRC: 0.991883 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.02      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt'  maml.exe Test eval=BinaryClassifier{threshold=0.95 useRawScore=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.withThreshold-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 198 | 41 | 0.8285    negative || 3 | 441 | 0.9932    ||======================    Precision || 0.9851 | 0.9149 |    OVERALL 0/1 ACCURACY: 0.935578    LOG LOSS/instance: 0.111003    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.881154    AUC: 0.996136      OVERALL RESULTS    ---------------------------------------    AUC: 0.996136 (0.0000)    Accuracy: 0.935578 (0.0000)    Positive precision: 0.985075 (0.0000)    Positive recall: 0.828452 (0.0000)    Negative precision: 0.914938 (0.0000)    Negative recall: 0.993243 (0.0000)    Log-loss: 0.111003 (0.0000)    Log-loss reduction: 0.881154 (0.0000)    F1 Score: 0.900000 (0.0000)    AUPRC: 0.991883 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.006      Suffix of length 34 compared against sequence of length 42  Running 'LogisticRegression' on 'breast-cancer'  Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt} norm=no threads- eval=BinaryClassifier{threshold=0.95 useRawScore=-}  maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} threads=- norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Not adding a normalizer.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 118 | 16 | 0.8806    negative || 3 | 217 | 0.9864    ||======================    Precision || 0.9752 | 0.9313 |    OVERALL 0/1 ACCURACY: 0.946328    LOG LOSS/instance: 0.143504    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.850048    AUC: 0.994132    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 81 | 24 | 0.7714    negative || 0 | 224 | 1.0000    ||======================    Precision || 1.0000 | 0.9032 |    OVERALL 0/1 ACCURACY: 0.927052    LOG LOSS/instance: 0.111794    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.876260    AUC: 0.997236      OVERALL RESULTS    ---------------------------------------    AUC: 0.995684 (0.0016)    Accuracy: 0.936690 (0.0096)    Positive precision: 0.987603 (0.0124)    Positive recall: 0.826013 (0.0546)    Negative precision: 0.917278 (0.0141)    Negative recall: 0.993182 (0.0068)    Log-loss: 0.127649 (0.0159)    Log-loss reduction: 0.863154 (0.0131)    F1 Score: 0.898229 (0.0273)    AUPRC: 0.991584 (0.0025)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.023      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-CV-breast-cancer.withThreshold.txt'  Test BinaryClassifierTesterThresholdingTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerTest with memory usage 135,069,696.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerTest [PASS]  Output:  Running 'WeightedEnsembleMulticlass' on 'iris'  Running as: TrainTest tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt xf=Term{col=Label} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-model.zip seed=1 xf=Term{col=Label}   Starting test: Microsoft.ML.RunTests.TestPredictors.RandomCalibratorPerceptronTest  Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 5 learners for the batch 1    Beginning training model 1 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 1 of 5 finished in 00:00:00.0241220    Beginning training model 2 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 2 of 5 finished in 00:00:00.0382289    Beginning training model 3 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 3 of 5 finished in 00:00:00.0257333    Beginning training model 4 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 10 of 15 weights.    Trainer 4 of 5 finished in 00:00:00.0314877    Beginning training model 5 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 5 of 5 finished in 00:00:00.0286821    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 45 | 5 | 0.9000    2 || 0 | 3 | 47 | 0.9400    ||========================    Precision ||1.0000 |0.9375 |0.9038 |    Accuracy(micro-avg): 0.946667    Accuracy(macro-avg): 0.946667    Log-loss: 0.433342    Log-loss reduction: 0.605555      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.946667 (0.0000)    Accuracy(macro-avg): 0.946667 (0.0000)    Log-loss: 0.433342 (0.0000)    Log-loss reduction: 0.605555 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.169      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris-out.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris-rp.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Average-TrainTest-iris.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Average-TrainTest-iris.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Average-TrainTest-iris-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 45 | 5 | 0.9000    2 || 0 | 3 | 47 | 0.9400    ||========================    Precision ||1.0000 |0.9375 |0.9038 |    Accuracy(micro-avg): 0.946667    Accuracy(macro-avg): 0.946667    Log-loss: 0.433342    Log-loss reduction: 0.605555      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.946667 (0.0000)    Accuracy(macro-avg): 0.946667 (0.0000)    Log-loss: 0.433342 (0.0000)    Log-loss reduction: 0.605555 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.01      Suffix of length 27 compared against sequence of length 61  Test EnsemblesMultiAveragerTest: completed normally: passed  Test EnsemblesMultiAveragerTest is using osx-arm64 configuration specific baselines.  Microsoft.ML.RunTests.TestPredictors.MulticlassifierLightGBMKeyLabelTest [SKIP]  LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.RandomCalibratorPerceptronTest with memory usage 135,069,696.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.RandomCalibratorPerceptronTest [PASS]  Output:  Running 'AveragedPerceptron' on 'breast-cancer'  Running as: TrainTest tr=AveragedPerceptron data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt} numcali=200 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesAveragerCombinerTest  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=AveragedPerceptron numcali=200 dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter)    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 234 | 5 | 0.9791    negative || 12 | 432 | 0.9730    ||======================    Precision || 0.9512 | 0.9886 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.120617    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.870860    AUC: 0.996146      OVERALL RESULTS    ---------------------------------------    AUC: 0.996146 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.951220 (0.0000)    Positive recall: 0.979079 (0.0000)    Negative precision: 0.988558 (0.0000)    Negative recall: 0.972973 (0.0000)    Log-loss: 0.120617 (0.0000)    Log-loss reduction: 0.870860 (0.0000)    F1 Score: 0.964948 (0.0000)    AUPRC: 0.992065 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.016      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 234 | 5 | 0.9791    negative || 12 | 432 | 0.9730    ||======================    Precision || 0.9512 | 0.9886 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.120617    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.870860    AUC: 0.996146      OVERALL RESULTS    ---------------------------------------    AUC: 0.996146 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.951220 (0.0000)    Positive recall: 0.979079 (0.0000)    Negative precision: 0.988558 (0.0000)    Negative recall: 0.972973 (0.0000)    Log-loss: 0.120617 (0.0000)    Log-loss reduction: 0.870860 (0.0000)    F1 Score: 0.964948 (0.0000)    AUPRC: 0.992065 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.006      Suffix of length 34 compared against sequence of length 38  Running 'AveragedPerceptron' on 'breast-cancer'  Running as: CV tr=AveragedPerceptron data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt} threads- numcali=200  maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)    Training calibrator.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 133 | 1 | 0.9925    negative || 9 | 211 | 0.9591    ||======================    Precision || 0.9366 | 0.9953 |    OVERALL 0/1 ACCURACY: 0.971751    LOG LOSS/instance: 0.139629    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.854097    AUC: 0.994403    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 100 | 5 | 0.9524    negative || 3 | 221 | 0.9866    ||======================    Precision || 0.9709 | 0.9779 |    OVERALL 0/1 ACCURACY: 0.975684    LOG LOSS/instance: 0.121001    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.866069    AUC: 0.997619      OVERALL RESULTS    ---------------------------------------    AUC: 0.996011 (0.0016)    Accuracy: 0.973718 (0.0020)    Positive precision: 0.953747 (0.0171)    Positive recall: 0.972459 (0.0201)    Negative precision: 0.986580 (0.0087)    Negative recall: 0.972849 (0.0138)    Log-loss: 0.130315 (0.0093)    Log-loss reduction: 0.860083 (0.0060)    F1 Score: 0.962653 (0.0011)    AUPRC: 0.992269 (0.0025)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.017      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt'  Test RandomCalibratorPerceptronTest: completed normally: passed  Test RandomCalibratorPerceptronTest is using osx-arm64 configuration specific baselines.  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierFieldAwareFactorizationMachineTest [SKIP]  FieldAwareFactorizationMachine doesn't currently support non x86/x64. https://github.com/dotnet/machinelearning/issues/5871 Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesAveragerCombinerTest with memory usage 137,330,688.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesAveragerCombinerTest [PASS]  Output:  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{nm=20 oc=Average tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Average-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{nm=20 oc=Average tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Average-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-model.zip seed=1 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiClassBootstrapSelectorTest    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 20 learners for the batch 1    Beginning training model 1 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter)    Trainer 1 of 20 finished in 00:00:00.0012939    Beginning training model 2 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 2 of 20 finished in 00:00:00.0008392    Beginning training model 3 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter)    Trainer 3 of 20 finished in 00:00:00.0007009    Beginning training model 4 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 4 of 20 finished in 00:00:00.0007130    Beginning training model 5 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 5 of 20 finished in 00:00:00.0007100    Beginning training model 6 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 6 of 20 finished in 00:00:00.0007126    Beginning training model 7 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 7 of 20 finished in 00:00:00.0006979    Beginning training model 8 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 8 of 20 finished in 00:00:00.0006858    Beginning training model 9 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 9 of 20 finished in 00:00:00.0007375    Beginning training model 10 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 10 of 20 finished in 00:00:00.0028677    Beginning training model 11 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 11 of 20 finished in 00:00:00.0006947    Beginning training model 12 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)    Trainer 12 of 20 finished in 00:00:00.0007220    Beginning training model 13 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 13 of 20 finished in 00:00:00.0006925    Beginning training model 14 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter)    Trainer 14 of 20 finished in 00:00:00.0006910    Beginning training model 15 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 15 of 20 finished in 00:00:00.0007126    Beginning training model 16 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 16 of 20 finished in 00:00:00.0007107    Beginning training model 17 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 17 of 20 finished in 00:00:00.0006833    Beginning training model 18 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter)    Trainer 18 of 20 finished in 00:00:00.0006866    Beginning training model 19 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter)    Trainer 19 of 20 finished in 00:00:00.0006826    Beginning training model 20 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 20 of 20 finished in 00:00:00.0007125    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9585 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.116558    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.875206    AUC: 0.995920      OVERALL RESULTS    ---------------------------------------    AUC: 0.995920 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.958506 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981900 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.116558 (0.0000)    Log-loss reduction: 0.875206 (0.0000)    F1 Score: 0.962500 (0.0000)    AUPRC: 0.991714 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.046      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-Average-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-Average-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Average-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Average-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-Average-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Average-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Average-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9585 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.116558    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.875206    AUC: 0.995920      OVERALL RESULTS    ---------------------------------------    AUC: 0.995920 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.958506 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981900 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.116558 (0.0000)    Log-loss reduction: 0.875206 (0.0000)    F1 Score: 0.962500 (0.0000)    AUPRC: 0.991714 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:12 PM Time elapsed(s): 0.022      Suffix of length 34 compared against sequence of length 118  Test EnsemblesAveragerCombinerTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiClassBootstrapSelectorTest with memory usage 137,936,896.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiClassBootstrapSelectorTest [PASS]  Output:  Running 'WeightedEnsembleMulticlass' on 'iris' Starting test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationTest  Running as: TrainTest tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt xf=Term{col=Label} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-model.zip seed=1 xf=Term{col=Label}    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 20 learners for the batch 1    Beginning training model 1 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 1 of 20 finished in 00:00:00.0224520    Beginning training model 2 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 2 of 20 finished in 00:00:00.0333244    Beginning training model 3 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 3 of 20 finished in 00:00:00.0250762    Beginning training model 4 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 10 of 15 weights.    Trainer 4 of 20 finished in 00:00:00.0303159    Beginning training model 5 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 5 of 20 finished in 00:00:00.0286702    Beginning training model 6 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 6 of 20 finished in 00:00:00.0244781    Beginning training model 7 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 7 of 20 finished in 00:00:00.0326853    Beginning training model 8 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 13 of 15 weights.    Trainer 8 of 20 finished in 00:00:00.0328437    Beginning training model 9 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 9 of 20 finished in 00:00:00.0591335    Beginning training model 10 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 10 of 20 finished in 00:00:00.0304761    Beginning training model 11 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 11 of 20 finished in 00:00:00.0565515    Beginning training model 12 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 12 of 20 finished in 00:00:00.0196605    Beginning training model 13 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 10 of 15 weights.    Trainer 13 of 20 finished in 00:00:00.0166334    Beginning training model 14 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 14 of 20 finished in 00:00:00.0245287    Beginning training model 15 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 15 of 20 finished in 00:00:00.0296570    Beginning training model 16 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 16 of 20 finished in 00:00:00.0382781    Beginning training model 17 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 10 of 15 weights.    Trainer 17 of 20 finished in 00:00:00.0269268    Beginning training model 18 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 18 of 20 finished in 00:00:00.0246242    Beginning training model 19 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 19 of 20 finished in 00:00:00.0275857    Beginning training model 20 of 20    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 20 of 20 finished in 00:00:00.0314066    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 45 | 5 | 0.9000    2 || 0 | 4 | 46 | 0.9200    ||========================    Precision ||1.0000 |0.9184 |0.9020 |    Accuracy(micro-avg): 0.940000    Accuracy(macro-avg): 0.940000    Log-loss: 0.435527    Log-loss reduction: 0.603567      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.940000 (0.0000)    Accuracy(macro-avg): 0.940000 (0.0000)    Log-loss: 0.435527 (0.0000)    Log-loss reduction: 0.603567 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:13 PM Time elapsed(s): 0.64      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris-out.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris-rp.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Bootstrap-TrainTest-iris.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Bootstrap-TrainTest-iris-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 45 | 5 | 0.9000    2 || 0 | 4 | 46 | 0.9200    ||========================    Precision ||1.0000 |0.9184 |0.9020 |    Accuracy(micro-avg): 0.940000    Accuracy(macro-avg): 0.940000    Log-loss: 0.435527    Log-loss reduction: 0.603567      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.940000 (0.0000)    Accuracy(macro-avg): 0.940000 (0.0000)    Log-loss: 0.435527 (0.0000)    Log-loss reduction: 0.603567 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:13 PM Time elapsed(s): 0.013      Suffix of length 27 compared against sequence of length 151  Test EnsemblesMultiClassBootstrapSelectorTest: completed normally: passed  Test EnsemblesMultiClassBootstrapSelectorTest is using osx-arm64 configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationTest with memory usage 138,133,504.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmTest  Running 'FastTreeBinaryClassification' on 'breast-cancer'  Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} cache=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Processed 683 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Reserved memory for tree learner: 3852 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 240 | 1 | 0.9959    negative || 13 | 445 | 0.9716    ||======================    Precision || 0.9486 | 0.9978 |    OVERALL 0/1 ACCURACY: 0.979971    LOG LOSS/instance: 0.092572    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.900387    AUC: 0.995370      OVERALL RESULTS    ---------------------------------------    AUC: 0.995370 (0.0000)    Accuracy: 0.979971 (0.0000)    Positive precision: 0.948617 (0.0000)    Positive recall: 0.995851 (0.0000)    Negative precision: 0.997758 (0.0000)    Negative recall: 0.971616 (0.0000)    Log-loss: 0.092572 (0.0000)    Log-loss reduction: 0.900387 (0.0000)    F1 Score: 0.971660 (0.0000)    AUPRC: 0.970606 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:13 PM Time elapsed(s): 0.035      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 240 | 1 | 0.9959    negative || 13 | 445 | 0.9716    ||======================    Precision || 0.9486 | 0.9978 |    OVERALL 0/1 ACCURACY: 0.979971    LOG LOSS/instance: 0.092572    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.900387    AUC: 0.995370      OVERALL RESULTS    ---------------------------------------    AUC: 0.995370 (0.0000)    Accuracy: 0.979971 (0.0000)    Positive precision: 0.948617 (0.0000)    Positive recall: 0.995851 (0.0000)    Negative precision: 0.997758 (0.0000)    Negative recall: 0.971616 (0.0000)    Log-loss: 0.092572 (0.0000)    Log-loss reduction: 0.900387 (0.0000)    F1 Score: 0.971660 (0.0000)    AUPRC: 0.970606 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:13 PM Time elapsed(s): 0.009      Suffix of length 33 compared against sequence of length 45  Running 'FastTreeBinaryClassification' on 'breast-cancer'  Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 tdrop=0.5 lr=0.25 iter=20 mb=255} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=FastTreeBinaryClassification{nl=5 mil=5 tdrop=0.5 lr=0.25 iter=20 mb=255} cache=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Processed 683 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Reserved memory for tree learner: 3852 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 234 | 7 | 0.9710    negative || 28 | 430 | 0.9389    ||======================    Precision || 0.8931 | 0.9840 |    OVERALL 0/1 ACCURACY: 0.949928    LOG LOSS/instance: 0.626065    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.326318    AUC: 0.987371      OVERALL RESULTS    ---------------------------------------    AUC: 0.987371 (0.0000)    Accuracy: 0.949928 (0.0000)    Positive precision: 0.893130 (0.0000)    Positive recall: 0.970954 (0.0000)    Negative precision: 0.983982 (0.0000)    Negative recall: 0.938865 (0.0000)    Log-loss: 0.626065 (0.0000)    Log-loss reduction: 0.326318 (0.0000)    F1 Score: 0.930417 (0.0000)    AUPRC: 0.942315 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:13 PM Time elapsed(s): 0.034      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDrop-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 234 | 7 | 0.9710    negative || 28 | 430 | 0.9389    ||======================    Precision || 0.8931 | 0.9840 |    OVERALL 0/1 ACCURACY: 0.949928    LOG LOSS/instance: 0.626065    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.326318    AUC: 0.987371      OVERALL RESULTS    ---------------------------------------    AUC: 0.987371 (0.0000)    Accuracy: 0.949928 (0.0000)    Positive precision: 0.893130 (0.0000)    Positive recall: 0.970954 (0.0000)    Negative precision: 0.983982 (0.0000)    Negative recall: 0.938865 (0.0000)    Log-loss: 0.626065 (0.0000)    Log-loss reduction: 0.326318 (0.0000)    F1 Score: 0.930417 (0.0000)    AUPRC: 0.942315 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:13 PM Time elapsed(s): 0.007      Suffix of length 33 compared against sequence of length 45  Running 'FastTreeBinaryClassification' on 'breast-cancer'  Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 bsr+ lr=0.25 iter=20 mb=255} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=FastTreeBinaryClassification{nl=5 mil=5 bsr+ lr=0.25 iter=20 mb=255} cache=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Processed 683 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Reserved memory for tree learner: 3852 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 233 | 8 | 0.9668    negative || 28 | 430 | 0.9389    ||======================    Precision || 0.8927 | 0.9817 |    OVERALL 0/1 ACCURACY: 0.948498    LOG LOSS/instance: 0.837162    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.099166    AUC: 0.983973      OVERALL RESULTS    ---------------------------------------    AUC: 0.983973 (0.0000)    Accuracy: 0.948498 (0.0000)    Positive precision: 0.892720 (0.0000)    Positive recall: 0.966805 (0.0000)    Negative precision: 0.981735 (0.0000)    Negative recall: 0.938865 (0.0000)    Log-loss: 0.837162 (0.0000)    Log-loss reduction: 0.099166 (0.0000)    F1 Score: 0.928287 (0.0000)    AUPRC: 0.939241 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:13 PM Time elapsed(s): 0.03      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeBsr-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 233 | 8 | 0.9668    negative || 28 | 430 | 0.9389    ||======================    Precision || 0.8927 | 0.9817 |    OVERALL 0/1 ACCURACY: 0.948498    LOG LOSS/instance: 0.837162    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.099166    AUC: 0.983973      OVERALL RESULTS    ---------------------------------------    AUC: 0.983973 (0.0000)    Accuracy: 0.948498 (0.0000)    Positive precision: 0.892720 (0.0000)    Positive recall: 0.966805 (0.0000)    Negative precision: 0.981735 (0.0000)    Negative recall: 0.938865 (0.0000)    Log-loss: 0.837162 (0.0000)    Log-loss reduction: 0.099166 (0.0000)    F1 Score: 0.928287 (0.0000)    AUPRC: 0.939241 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:13 PM Time elapsed(s): 0.008      Suffix of length 33 compared against sequence of length 45  Running 'FastTreeBinaryClassification' on 'breast-cancer'  Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} cache=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise on disk    Warning:  16 of 699 examples will be skipped due to missing feature values    Processed 683 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise on disk    Warning:  16 of 699 examples will be skipped due to missing feature values    Reserved memory for tree learner: 3852 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 240 | 1 | 0.9959    negative || 13 | 445 | 0.9716    ||======================    Precision || 0.9486 | 0.9978 |    OVERALL 0/1 ACCURACY: 0.979971    LOG LOSS/instance: 0.092572    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.900387    AUC: 0.995370      OVERALL RESULTS    ---------------------------------------    AUC: 0.995370 (0.0000)    Accuracy: 0.979971 (0.0000)    Positive precision: 0.948617 (0.0000)    Positive recall: 0.995851 (0.0000)    Negative precision: 0.997758 (0.0000)    Negative recall: 0.971616 (0.0000)    Log-loss: 0.092572 (0.0000)    Log-loss reduction: 0.900387 (0.0000)    F1 Score: 0.971660 (0.0000)    AUPRC: 0.970606 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:13 PM Time elapsed(s): 0.045      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 240 | 1 | 0.9959    negative || 13 | 445 | 0.9716    ||======================    Precision || 0.9486 | 0.9978 |    OVERALL 0/1 ACCURACY: 0.979971    LOG LOSS/instance: 0.092572    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.900387    AUC: 0.995370      OVERALL RESULTS    ---------------------------------------    AUC: 0.995370 (0.0000)    Accuracy: 0.979971 (0.0000)    Positive precision: 0.948617 (0.0000)    Positive recall: 0.995851 (0.0000)    Negative precision: 0.997758 (0.0000)    Negative recall: 0.971616 (0.0000)    Log-loss: 0.092572 (0.0000)    Log-loss reduction: 0.900387 (0.0000)    F1 Score: 0.971660 (0.0000)    AUPRC: 0.970606 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:13 PM Time elapsed(s): 0.007      Suffix of length 33 compared against sequence of length 45  Test FastTreeBinaryClassificationTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmTest with memory usage 141,361,152.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmTest [PASS]  Output:  Running 'LdSvm' on 'breast-cancer'  Running as: TrainTest tr=LdSvm{iter=1000} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=LdSvm{iter=1000} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Starting test: Microsoft.ML.RunTests.TestPredictors.PAVCalibratorPerceptronTest    Warning:  Skipped 16 rows with missing feature/label values    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 7 | 0.9707    negative || 11 | 433 | 0.9752    ||======================    Precision || 0.9547 | 0.9841 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.111359    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.880773    AUC: 0.996127      OVERALL RESULTS    ---------------------------------------    AUC: 0.996127 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.954733 (0.0000)    Positive recall: 0.970711 (0.0000)    Negative precision: 0.984091 (0.0000)    Negative recall: 0.975225 (0.0000)    Log-loss: 0.111359 (0.0000)    Log-loss reduction: 0.880773 (0.0000)    F1 Score: 0.962656 (0.0000)    AUPRC: 0.992120 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.306      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-TrainTest-breast-cancer-out.txt  Output matches baseline: 'LdSvm/LDSVM-def-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/netcoreapp/LDSVM-def-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'LdSvm/LDSVM-def-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-TrainTest-breast-cancer.txt  Output matches baseline: 'LdSvm/LDSVM-def-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 7 | 0.9707    negative || 11 | 433 | 0.9752    ||======================    Precision || 0.9547 | 0.9841 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.111359    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.880773    AUC: 0.996127      OVERALL RESULTS    ---------------------------------------    AUC: 0.996127 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.954733 (0.0000)    Positive recall: 0.970711 (0.0000)    Negative precision: 0.984091 (0.0000)    Negative recall: 0.975225 (0.0000)    Log-loss: 0.111359 (0.0000)    Log-loss reduction: 0.880773 (0.0000)    F1 Score: 0.962656 (0.0000)    AUPRC: 0.992120 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.009      Suffix of length 34 compared against sequence of length 38  Running 'LdSvm' on 'breast-cancer'  Running as: CV tr=LdSvm{iter=1000} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-CV-breast-cancer.txt} threads-  maml.exe CV tr=LdSvm{iter=1000} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 8 rows with missing feature/label values    Training calibrator.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 8 rows with missing feature/label values    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 134 | 0 | 1.0000    negative || 10 | 210 | 0.9545    ||======================    Precision || 0.9306 | 1.0000 |    OVERALL 0/1 ACCURACY: 0.971751    LOG LOSS/instance: 0.125103    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.869275    AUC: 0.994369    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 94 | 11 | 0.8952    negative || 4 | 220 | 0.9821    ||======================    Precision || 0.9592 | 0.9524 |    OVERALL 0/1 ACCURACY: 0.954407    LOG LOSS/instance: 0.283104    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.686643    AUC: 0.951786      OVERALL RESULTS    ---------------------------------------    AUC: 0.973077 (0.0213)    Accuracy: 0.963079 (0.0087)    Positive precision: 0.944870 (0.0143)    Positive recall: 0.947619 (0.0524)    Negative precision: 0.976190 (0.0238)    Negative recall: 0.968344 (0.0138)    Log-loss: 0.204103 (0.0790)    Log-loss reduction: 0.777959 (0.0913)    F1 Score: 0.945069 (0.0190)    AUPRC: 0.974864 (0.0146)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.399      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer-out.txt  Output matches baseline: 'LdSvm/LDSVM-def-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer-rp.txt  Output matches baseline: 'LdSvm/LDSVM-def-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LdSvm/LDSVM-def-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LdSvm/osx-arm64/LDSVM-def-CV-breast-cancer.txt  Output matches baseline: 'LdSvm/LDSVM-def-CV-breast-cancer.txt'  Test BinaryClassifierLDSvmTest: completed normally: passed  Test BinaryClassifierLDSvmTest is using osx-arm64 configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.PAVCalibratorPerceptronTest with memory usage 141,377,536.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.PAVCalibratorPerceptronTest [PASS]  Output:  Running 'AveragedPerceptron' on 'breast-cancer'  Running as: TrainTest tr=AveragedPerceptron data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt} cali=PAV  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=AveragedPerceptron cali=PAV dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-model.zip seed=1  Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNormTest   Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter)    Training calibrator.    PAV calibrator: piecewise function approximation has 9 components.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 234 | 5 | 0.9791    negative || 12 | 432 | 0.9730    ||======================    Precision || 0.9512 | 0.9886 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.084507    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.909522    AUC: 0.996146      OVERALL RESULTS    ---------------------------------------    AUC: 0.996146 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.951220 (0.0000)    Positive recall: 0.979079 (0.0000)    Negative precision: 0.988558 (0.0000)    Negative recall: 0.972973 (0.0000)    Log-loss: 0.084507 (0.0000)    Log-loss reduction: 0.909522 (0.0000)    F1 Score: 0.964948 (0.0000)    AUPRC: 0.992065 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.019      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 234 | 5 | 0.9791    negative || 12 | 432 | 0.9730    ||======================    Precision || 0.9512 | 0.9886 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.084507    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.909522    AUC: 0.996146      OVERALL RESULTS    ---------------------------------------    AUC: 0.996146 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.951220 (0.0000)    Positive recall: 0.979079 (0.0000)    Negative precision: 0.988558 (0.0000)    Negative recall: 0.972973 (0.0000)    Log-loss: 0.084507 (0.0000)    Log-loss reduction: 0.909522 (0.0000)    F1 Score: 0.964948 (0.0000)    AUPRC: 0.992065 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.018      Suffix of length 34 compared against sequence of length 39  Running 'AveragedPerceptron' on 'breast-cancer'  Running as: CV tr=AveragedPerceptron data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt} threads- cali=PAV  maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)    Training calibrator.    PAV calibrator: piecewise function approximation has 5 components.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter)    Training calibrator.    PAV calibrator: piecewise function approximation has 6 components.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 133 | 1 | 0.9925    negative || 9 | 211 | 0.9591    ||======================    Precision || 0.9366 | 0.9953 |    OVERALL 0/1 ACCURACY: 0.971751    LOG LOSS/instance: Infinity    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): -Infinity    AUC: 0.994403    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 100 | 5 | 0.9524    negative || 3 | 221 | 0.9866    ||======================    Precision || 0.9709 | 0.9779 |    OVERALL 0/1 ACCURACY: 0.975684    LOG LOSS/instance: 0.227705    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.747961    AUC: 0.997619      OVERALL RESULTS    ---------------------------------------    AUC: 0.996011 (0.0016)    Accuracy: 0.973718 (0.0020)    Positive precision: 0.953747 (0.0171)    Positive recall: 0.972459 (0.0201)    Negative precision: 0.986580 (0.0087)    Negative recall: 0.972849 (0.0138)    Log-loss: Infinity (NaN)    Log-loss reduction: -Infinity (NaN)    F1 Score: 0.962653 (0.0011)    AUPRC: 0.992269 (0.0025)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.024      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/netcoreapp/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/osx-arm64/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt'  Test PAVCalibratorPerceptronTest: completed normally: passed  Test PAVCalibratorPerceptronTest is using osx-arm64 configuration specific baselines.  Microsoft.ML.RunTests.TestPredictors.RegressorOlsTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNormTest with memory usage 141,377,536.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNormTest [PASS]  Output:  Running 'LogisticRegression' on 'breast-cancer'  Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer-model.zip seed=1 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesStackingCombinerTest    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 16 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 228 | 11 | 0.9540    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9620 | 0.9753 |    OVERALL 0/1 ACCURACY: 0.970717    LOG LOSS/instance: 0.119042    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.872546    AUC: 0.996108      OVERALL RESULTS    ---------------------------------------    AUC: 0.996108 (0.0000)    Accuracy: 0.970717 (0.0000)    Positive precision: 0.962025 (0.0000)    Positive recall: 0.953975 (0.0000)    Negative precision: 0.975336 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.119042 (0.0000)    Log-loss reduction: 0.872546 (0.0000)    F1 Score: 0.957983 (0.0000)    AUPRC: 0.992030 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.019      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 228 | 11 | 0.9540    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9620 | 0.9753 |    OVERALL 0/1 ACCURACY: 0.970717    LOG LOSS/instance: 0.119042    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.872546    AUC: 0.996108      OVERALL RESULTS    ---------------------------------------    AUC: 0.996108 (0.0000)    Accuracy: 0.970717 (0.0000)    Positive precision: 0.962025 (0.0000)    Positive recall: 0.953975 (0.0000)    Negative precision: 0.975336 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.119042 (0.0000)    Log-loss reduction: 0.872546 (0.0000)    F1 Score: 0.957983 (0.0000)    AUPRC: 0.992030 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.008      Suffix of length 34 compared against sequence of length 42  Running 'LogisticRegression' on 'breast-cancer'  Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt} threads-  maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 8 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 129 | 5 | 0.9627    negative || 7 | 213 | 0.9682    ||======================    Precision || 0.9485 | 0.9771 |    OVERALL 0/1 ACCURACY: 0.966102    LOG LOSS/instance: 0.137058    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.856784    AUC: 0.994166    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 96 | 9 | 0.9143    negative || 3 | 221 | 0.9866    ||======================    Precision || 0.9697 | 0.9609 |    OVERALL 0/1 ACCURACY: 0.963526    LOG LOSS/instance: 0.130675    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.855361    AUC: 0.997279      OVERALL RESULTS    ---------------------------------------    AUC: 0.995722 (0.0016)    Accuracy: 0.964814 (0.0013)    Positive precision: 0.959113 (0.0106)    Positive recall: 0.938486 (0.0242)    Negative precision: 0.968967 (0.0081)    Negative recall: 0.977394 (0.0092)    Log-loss: 0.133866 (0.0032)    Log-loss reduction: 0.856072 (0.0007)    F1 Score: 0.948366 (0.0072)    AUPRC: 0.991520 (0.0025)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.022      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-norm-CV-breast-cancer-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-norm-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-norm-CV-breast-cancer-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-norm-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-norm-CV-breast-cancer.txt'  Test BinaryClassifierLogisticRegressionNormTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.WeightingRegressionPredictorsTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesStackingCombinerTest with memory usage 141,443,072.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesStackingCombinerTest [PASS]  Output:  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{nm=5 oc=Stacking{bp=ap} tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBestPerformanceSelectorTest  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{nm=5 oc=Stacking{bp=ap} tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 5 learners for the batch 1    Beginning training model 1 of 5    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 1 of 5 finished in 00:00:00.0016157    Beginning training model 2 of 5    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 2 of 5 finished in 00:00:00.0036308    Beginning training model 3 of 5    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 3 of 5 finished in 00:00:00.0007674    Beginning training model 4 of 5    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 4 of 5 finished in 00:00:00.0007268    Beginning training model 5 of 5    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 5 of 5 finished in 00:00:00.0007167    The number of instances used for stacking trainer is 213    Warning:  The trainer specified for stacking wants normalization, but we do not currently allow this.    Warning:  Skipped 40 instances with missing features during training (over 10 iterations; 4 inst/iter)    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9585 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.116054    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.875745    AUC: 0.996023      OVERALL RESULTS    ---------------------------------------    AUC: 0.996023 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.958506 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981900 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.116054 (0.0000)    Log-loss reduction: 0.875745 (0.0000)    F1 Score: 0.962500 (0.0000)    AUPRC: 0.991901 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.038      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-StackingAP-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9585 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.116054    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.875745    AUC: 0.996023      OVERALL RESULTS    ---------------------------------------    AUC: 0.996023 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.958506 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981900 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.116054 (0.0000)    Log-loss reduction: 0.875745 (0.0000)    F1 Score: 0.962500 (0.0000)    AUPRC: 0.991901 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:14 PM Time elapsed(s): 0.014      Suffix of length 34 compared against sequence of length 61  Test EnsemblesStackingCombinerTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.MulticlassLRSparseTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBestPerformanceSelectorTest with memory usage 141,967,360.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesBestPerformanceSelectorTest [PASS]  Output:  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{nm=20 pt=BestPerformanceSelector tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{nm=20 pt=BestPerformanceSelector tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-model.zip seed=1   Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesAllDataSetSelectorTest  Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 20 learners for the batch 1    Beginning training model 1 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 1 of 20 finished in 00:00:00.0080311    Beginning training model 2 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 2 of 20 finished in 00:00:00.0018587    Beginning training model 3 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 3 of 20 finished in 00:00:00.0015332    Beginning training model 4 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 4 of 20 finished in 00:00:00.0015541    Beginning training model 5 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 5 of 20 finished in 00:00:00.0014801    Beginning training model 6 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 6 of 20 finished in 00:00:00.0063286    Beginning training model 7 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 7 of 20 finished in 00:00:00.0015444    Beginning training model 8 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 8 of 20 finished in 00:00:00.0014661    Beginning training model 9 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)    Trainer 9 of 20 finished in 00:00:00.0015305    Beginning training model 10 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 10 of 20 finished in 00:00:00.0015285    Beginning training model 11 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter)    Trainer 11 of 20 finished in 00:00:00.0014622    Beginning training model 12 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 12 of 20 finished in 00:00:00.0015557    Beginning training model 13 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 13 of 20 finished in 00:00:00.0032076    Beginning training model 14 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 14 of 20 finished in 00:00:00.0024045    Beginning training model 15 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter)    Trainer 15 of 20 finished in 00:00:00.0023613    Beginning training model 16 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter)    Trainer 16 of 20 finished in 00:00:00.0024009    Beginning training model 17 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 17 of 20 finished in 00:00:00.0023918    Beginning training model 18 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 20 instances with missing features during training (over 1 iterations; 20 inst/iter)    Trainer 18 of 20 finished in 00:00:00.0050407    Beginning training model 19 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)    Trainer 19 of 20 finished in 00:00:00.0016164    Beginning training model 20 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 20 of 20 finished in 00:00:00.0017801    List of models and the metrics after sorted    | AUC(Sorted) || Name of Model |    | 0.9969167523124358 |LinearBinaryModelParameters    | 0.9966598150051388 |LinearBinaryModelParameters    | 0.9966598150051388 |LinearBinaryModelParameters    | 0.9966598150051388 |LinearBinaryModelParameters    | 0.9965313463514902 |LinearBinaryModelParameters    | 0.9964028776978417 |LinearBinaryModelParameters    | 0.9964028776978417 |LinearBinaryModelParameters    | 0.9962744090441932 |LinearBinaryModelParameters    | 0.9962744090441932 |LinearBinaryModelParameters    | 0.9961459403905447 |LinearBinaryModelParameters    | 0.9961459403905447 |LinearBinaryModelParameters    | 0.9961459403905447 |LinearBinaryModelParameters    | 0.9958890030832477 |LinearBinaryModelParameters    | 0.9957605344295992 |LinearBinaryModelParameters    | 0.9956320657759506 |LinearBinaryModelParameters    | 0.9955035971223022 |LinearBinaryModelParameters    | 0.9952466598150052 |LinearBinaryModelParameters    | 0.9947327852004111 |LinearBinaryModelParameters    | 0.994218910585817 |LinearBinaryModelParameters    | 0.9939619732785201 |LinearBinaryModelParameters    Warning:  10 of 20 trainings failed.    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 229 | 10 | 0.9582    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9622 | 0.9775 |    OVERALL 0/1 ACCURACY: 0.972182    LOG LOSS/instance: 0.117306    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.874405    AUC: 0.996042      OVERALL RESULTS    ---------------------------------------    AUC: 0.996042 (0.0000)    Accuracy: 0.972182 (0.0000)    Positive precision: 0.962185 (0.0000)    Positive recall: 0.958159 (0.0000)    Negative precision: 0.977528 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.117306 (0.0000)    Log-loss reduction: 0.874405 (0.0000)    F1 Score: 0.960168 (0.0000)    AUPRC: 0.991960 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.076      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-BestPerf-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 229 | 10 | 0.9582    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9622 | 0.9775 |    OVERALL 0/1 ACCURACY: 0.972182    LOG LOSS/instance: 0.117306    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.874405    AUC: 0.996042      OVERALL RESULTS    ---------------------------------------    AUC: 0.996042 (0.0000)    Accuracy: 0.972182 (0.0000)    Positive precision: 0.962185 (0.0000)    Positive recall: 0.958159 (0.0000)    Negative precision: 0.977528 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.117306 (0.0000)    Log-loss reduction: 0.874405 (0.0000)    F1 Score: 0.960168 (0.0000)    AUPRC: 0.991960 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.014      Suffix of length 34 compared against sequence of length 141  Test EnsemblesBestPerformanceSelectorTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.MulticlassCVTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestPredictors.RegressorLightGBMMAETest [SKIP]  LightGBM is 64-bit only  Microsoft.ML.RunTests.TestPredictors.MulticlassSdcaTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestPredictors.NnConfigTests [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesAllDataSetSelectorTest with memory usage 141,983,744.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesAllDataSetSelectorTest [PASS]  Output:  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{nm=20 st=AllInstanceSelector tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-All-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-All-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{nm=20 st=AllInstanceSelector tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-All-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-All-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.  Starting test: Microsoft.ML.RunTests.TestPredictors.FastTreeHighMinDocsTest   Training 20 learners for the batch 1    Beginning training model 1 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 1 of 20 finished in 00:00:00.0012489    Beginning training model 2 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 2 of 20 finished in 00:00:00.0005443    Beginning training model 3 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 3 of 20 finished in 00:00:00.0005304    Beginning training model 4 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 4 of 20 finished in 00:00:00.0005273    Beginning training model 5 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 5 of 20 finished in 00:00:00.0005249    Beginning training model 6 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 6 of 20 finished in 00:00:00.0005267    Beginning training model 7 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 7 of 20 finished in 00:00:00.0005205    Beginning training model 8 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 8 of 20 finished in 00:00:00.0005240    Beginning training model 9 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 9 of 20 finished in 00:00:00.0005223    Beginning training model 10 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 10 of 20 finished in 00:00:00.0005227    Beginning training model 11 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 11 of 20 finished in 00:00:00.0005180    Beginning training model 12 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 12 of 20 finished in 00:00:00.0005170    Beginning training model 13 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 13 of 20 finished in 00:00:00.0005217    Beginning training model 14 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 14 of 20 finished in 00:00:00.0005210    Beginning training model 15 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 15 of 20 finished in 00:00:00.0005192    Beginning training model 16 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 16 of 20 finished in 00:00:00.0005210    Beginning training model 17 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 17 of 20 finished in 00:00:00.0005197    Beginning training model 18 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 18 of 20 finished in 00:00:00.0005193    Beginning training model 19 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 19 of 20 finished in 00:00:00.0005224    Beginning training model 20 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 20 of 20 finished in 00:00:00.0005251    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 7 | 0.9707    negative || 11 | 433 | 0.9752    ||======================    Precision || 0.9547 | 0.9841 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.117326    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.874384    AUC: 0.995995      OVERALL RESULTS    ---------------------------------------    AUC: 0.995995 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.954733 (0.0000)    Positive recall: 0.970711 (0.0000)    Negative precision: 0.984091 (0.0000)    Negative recall: 0.975225 (0.0000)    Log-loss: 0.117326 (0.0000)    Log-loss reduction: 0.874384 (0.0000)    F1 Score: 0.962656 (0.0000)    AUPRC: 0.991908 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.044      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-All-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-All-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-All-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-All-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-All-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-All-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-All-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-All-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-All-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-All-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 7 | 0.9707    negative || 11 | 433 | 0.9752    ||======================    Precision || 0.9547 | 0.9841 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.117326    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.874384    AUC: 0.995995      OVERALL RESULTS    ---------------------------------------    AUC: 0.995995 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.954733 (0.0000)    Positive recall: 0.970711 (0.0000)    Negative precision: 0.984091 (0.0000)    Negative recall: 0.975225 (0.0000)    Log-loss: 0.117326 (0.0000)    Log-loss reduction: 0.874384 (0.0000)    F1 Score: 0.962656 (0.0000)    AUPRC: 0.991908 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.032      Suffix of length 34 compared against sequence of length 98  Test EnsemblesAllDataSetSelectorTest: completed normally: passed  Test EnsemblesAllDataSetSelectorTest is using netcoreapp configuration specific baselines.  Microsoft.ML.RunTests.TestPredictors.TestMulticlassEnsembleCombiner [SKIP]  LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.FastTreeHighMinDocsTest with memory usage 141,983,744.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.FastTreeHighMinDocsTest [PASS]  Output:  Running 'FastTreeBinaryClassification' on 'breast-cancer'  Running as: TrainTest tr=FastTreeBinaryClassification{mil=10000 iter=5} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=FastTreeBinaryClassification{mil=10000 iter=5} cache=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer. Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryPriorTest    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Processed 683 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Reserved memory for tree learner: 468 bytes    Starting to train ...    Warning:  5 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 0 | 241 | 0.0000    negative || 0 | 458 | 1.0000    ||======================    Precision || 0.0000 | 0.6552 |    OVERALL 0/1 ACCURACY: 0.655222    LOG LOSS/instance: 1.000000    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): -0.076058    AUC: 0.500000      OVERALL RESULTS    ---------------------------------------    AUC: 0.500000 (0.0000)    Accuracy: 0.655222 (0.0000)    Positive precision: 0.000000 (0.0000)    Positive recall: 0.000000 (0.0000)    Negative precision: 0.655222 (0.0000)    Negative recall: 1.000000 (0.0000)    Log-loss: 1.000000 (0.0000)    Log-loss reduction: -0.076058 (0.0000)    F1 Score: 0.000000 (0.0000)    AUPRC: 0.415719 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.026      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeHighMinDocs-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 0 | 241 | 0.0000    negative || 0 | 458 | 1.0000    ||======================    Precision || 0.0000 | 0.6552 |    OVERALL 0/1 ACCURACY: 0.655222    LOG LOSS/instance: 1.000000    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): -0.076058    AUC: 0.500000      OVERALL RESULTS    ---------------------------------------    AUC: 0.500000 (0.0000)    Accuracy: 0.655222 (0.0000)    Positive precision: 0.000000 (0.0000)    Positive recall: 0.000000 (0.0000)    Negative precision: 0.655222 (0.0000)    Negative recall: 1.000000 (0.0000)    Log-loss: 1.000000 (0.0000)    Log-loss reduction: -0.076058 (0.0000)    F1 Score: 0.000000 (0.0000)    AUPRC: 0.415719 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.006      Suffix of length 33 compared against sequence of length 46  Test FastTreeHighMinDocsTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.MulticlassLRTest [SKIP]  Currently flaky on non x86/x64 devices. Disabling until we figure it out. See https://github.com/dotnet/machinelearning/issues/6684 Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryPriorTest with memory usage 142,245,888.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryPriorTest [PASS]  Output:  Running 'PriorPredictor' on 'breast-cancer'  Running as: TrainTest tr=PriorPredictor data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:~} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt} Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionBinNormTest  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=PriorPredictor dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:~} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 0 | 241 | 0.0000    negative || 0 | 458 | 1.0000    ||======================    Precision || 0.0000 | 0.6552 |    OVERALL 0/1 ACCURACY: 0.655222    LOG LOSS/instance: 0.929318    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.000000    AUC: 0.500000      OVERALL RESULTS    ---------------------------------------    AUC: 0.500000 (0.0000)    Accuracy: 0.655222 (0.0000)    Positive precision: 0.000000 (0.0000)    Positive recall: 0.000000 (0.0000)    Negative precision: 0.655222 (0.0000)    Negative recall: 1.000000 (0.0000)    Log-loss: 0.929318 (0.0000)    Log-loss reduction: 0.000000 (0.0000)    F1 Score: 0.000000 (0.0000)    AUPRC: 0.415719 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.008      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt  Output matches baseline: 'PriorPredictor/BinaryPrior-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'PriorPredictor/BinaryPrior-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt  Output matches baseline: 'PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 0 | 241 | 0.0000    negative || 0 | 458 | 1.0000    ||======================    Precision || 0.0000 | 0.6552 |    OVERALL 0/1 ACCURACY: 0.655222    LOG LOSS/instance: 0.929318    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.000000    AUC: 0.500000      OVERALL RESULTS    ---------------------------------------    AUC: 0.500000 (0.0000)    Accuracy: 0.655222 (0.0000)    Positive precision: 0.000000 (0.0000)    Positive recall: 0.000000 (0.0000)    Negative precision: 0.655222 (0.0000)    Negative recall: 1.000000 (0.0000)    Log-loss: 0.929318 (0.0000)    Log-loss reduction: 0.000000 (0.0000)    F1 Score: 0.000000 (0.0000)    AUPRC: 0.415719 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.006      Suffix of length 33 compared against sequence of length 36  Running 'PriorPredictor' on 'breast-cancer'  Running as: CV tr=PriorPredictor data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 loader=Text{col=Label:BL:0 col=Features:~} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-CV-breast-cancer.txt} threads-  maml.exe CV tr=PriorPredictor threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-CV-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:~} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Not adding a normalizer.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 0 | 134 | 0.0000    negative || 0 | 228 | 1.0000    ||======================    Precision || 0.0000 | 0.6298 |    OVERALL 0/1 ACCURACY: 0.629834    LOG LOSS/instance: 0.959786    Test-set entropy (prior Log-Loss/instance): 0.950799    LOG-LOSS REDUCTION (RIG): -0.009452    AUC: 0.500000    TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 0 | 107 | 0.0000    negative || 0 | 230 | 1.0000    ||======================    Precision || 0.0000 | 0.6825 |    OVERALL 0/1 ACCURACY: 0.682493    LOG LOSS/instance: 0.910421    Test-set entropy (prior Log-Loss/instance): 0.901650    LOG-LOSS REDUCTION (RIG): -0.009727    AUC: 0.500000      OVERALL RESULTS    ---------------------------------------    AUC: 0.500000 (0.0000)    Accuracy: 0.656163 (0.0263)    Positive precision: 0.000000 (0.0000)    Positive recall: 0.000000 (0.0000)    Negative precision: 0.656163 (0.0263)    Negative recall: 1.000000 (0.0000)    Log-loss: 0.935104 (0.0247)    Log-loss reduction: -0.009590 (0.0001)    F1 Score: 0.000000 (0.0000)    AUPRC: 0.418968 (0.0212)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.011      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt  Output matches baseline: 'PriorPredictor/BinaryPrior-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt  Output matches baseline: 'PriorPredictor/BinaryPrior-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PriorPredictor/BinaryPrior-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PriorPredictor/BinaryPrior-CV-breast-cancer.txt  Output matches baseline: 'PriorPredictor/BinaryPrior-CV-breast-cancer.txt'  Test BinaryPriorTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionBinNormTest with memory usage 143,245,312.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionBinNormTest [PASS]  Output:  Running 'LogisticRegression' on 'breast-cancer'  Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt} xf=BinNormalizer{col=Features numBins=5} Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerSDCATest  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer-model.zip seed=1 xf=BinNormalizer{col=Features numBins=5}    Not adding a normalizer.    Warning:  Skipped 16 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 9 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 7 | 0.9707    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9587 | 0.9841 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.116898    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.874842    AUC: 0.995208      OVERALL RESULTS    ---------------------------------------    AUC: 0.995208 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.958678 (0.0000)    Positive recall: 0.970711 (0.0000)    Negative precision: 0.984127 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.116898 (0.0000)    Log-loss reduction: 0.874842 (0.0000)    F1 Score: 0.964657 (0.0000)    AUPRC: 0.990065 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.024      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-bin-norm-TrainTest-breast-cancer-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 7 | 0.9707    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9587 | 0.9841 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.116898    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.874842    AUC: 0.995208      OVERALL RESULTS    ---------------------------------------    AUC: 0.995208 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.958678 (0.0000)    Positive recall: 0.970711 (0.0000)    Negative precision: 0.984127 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.116898 (0.0000)    Log-loss reduction: 0.874842 (0.0000)    F1 Score: 0.964657 (0.0000)    AUPRC: 0.990065 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.008      Suffix of length 34 compared against sequence of length 42  Running 'LogisticRegression' on 'breast-cancer'  Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt} xf=BinNormalizer{col=Features numBins=5} threads-  maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 xf=BinNormalizer{col=Features numBins=5}    Not adding a normalizer.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 9 of 10 weights.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 129 | 5 | 0.9627    negative || 7 | 213 | 0.9682    ||======================    Precision || 0.9485 | 0.9771 |    OVERALL 0/1 ACCURACY: 0.966102    LOG LOSS/instance: 0.145463    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.848001    AUC: 0.992232    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 99 | 6 | 0.9429    negative || 3 | 221 | 0.9866    ||======================    Precision || 0.9706 | 0.9736 |    OVERALL 0/1 ACCURACY: 0.972644    LOG LOSS/instance: 0.123323    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.863498    AUC: 0.996769      OVERALL RESULTS    ---------------------------------------    AUC: 0.994500 (0.0023)    Accuracy: 0.969373 (0.0033)    Positive precision: 0.959559 (0.0110)    Positive recall: 0.952772 (0.0099)    Negative precision: 0.975316 (0.0017)    Negative recall: 0.977394 (0.0092)    Log-loss: 0.134393 (0.0111)    Log-loss reduction: 0.855749 (0.0077)    F1 Score: 0.956039 (0.0005)    AUPRC: 0.988987 (0.0037)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:15 PM Time elapsed(s): 0.022      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-bin-norm-CV-breast-cancer-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-bin-norm-CV-breast-cancer.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-bin-norm-CV-breast-cancer.txt'  Test BinaryClassifierLogisticRegressionBinNormTest: completed normally: passed  Test BinaryClassifierLogisticRegressionBinNormTest is using osx-arm64 configuration specific baselines.  Microsoft.ML.RunTests.TestPredictors.FastTreeRegressionTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerSDCATest with memory usage 155,009,024.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerSDCATest [PASS]  Output:  Running 'WeightedEnsembleMulticlass' on 'iris'  Running as: TrainTest tr=WeightedEnsembleMulticlass{bp=SDCAMC{nt=1} nm=5 oc=MultiAverage tp=-} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt xf=Term{col=Label} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=WeightedEnsembleMulticlass{bp=SDCAMC{nt=1} nm=5 oc=MultiAverage tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-model.zip seed=1 xf=Term{col=Label}    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.   Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNonNegativeTest  Training 5 learners for the batch 1    Beginning training model 1 of 5    Using 1 thread to train.    Automatically choosing a check frequency of 1.    Auto-tuning parameters: maxIterations = 10563.    Auto-tuning parameters: L2 = 2.6670152E-05.    Auto-tuning parameters: L1Threshold (L1/L2) = 0.    Using best model from iteration 958.    Trainer 1 of 5 finished in 00:00:00.7041757    Beginning training model 2 of 5    Using 1 thread to train.    Automatically choosing a check frequency of 1.    Auto-tuning parameters: maxIterations = 8928.    Auto-tuning parameters: L2 = 2.6668373E-05.    Auto-tuning parameters: L1Threshold (L1/L2) = 0.    Using best model from iteration 874.    Trainer 2 of 5 finished in 00:00:00.6604160    Beginning training model 3 of 5    Using 1 thread to train.    Automatically choosing a check frequency of 1.    Auto-tuning parameters: maxIterations = 9201.    Auto-tuning parameters: L2 = 2.6673779E-05.    Auto-tuning parameters: L1Threshold (L1/L2) = 0.    Using best model from iteration 754.    Trainer 3 of 5 finished in 00:00:00.3613562    Beginning training model 4 of 5    Using 1 thread to train.    Automatically choosing a check frequency of 1.    Auto-tuning parameters: maxIterations = 10344.    Auto-tuning parameters: L2 = 2.66688E-05.    Auto-tuning parameters: L1Threshold (L1/L2) = 0.    Using best model from iteration 976.    Trainer 4 of 5 finished in 00:00:00.3451972    Beginning training model 5 of 5    Using 1 thread to train.    Automatically choosing a check frequency of 1.    Auto-tuning parameters: maxIterations = 9315.    Auto-tuning parameters: L2 = 2.6674597E-05.    Auto-tuning parameters: L1Threshold (L1/L2) = 0.    Using best model from iteration 1058.    Trainer 5 of 5 finished in 00:00:00.1286041    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 48 | 2 | 0.9600    2 || 0 | 1 | 49 | 0.9800    ||========================    Precision ||1.0000 |0.9796 |0.9608 |    Accuracy(micro-avg): 0.980000    Accuracy(macro-avg): 0.980000    Log-loss: 0.061647    Log-loss reduction: 0.943887      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.980000 (0.0000)    Accuracy(macro-avg): 0.980000 (0.0000)    Log-loss: 0.061647 (0.0000)    Log-loss reduction: 0.943887 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 2.216      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-out.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-rp.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-SDCA-Average-TrainTest-iris-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 48 | 2 | 0.9600    2 || 0 | 1 | 49 | 0.9800    ||========================    Precision ||1.0000 |0.9796 |0.9608 |    Accuracy(micro-avg): 0.980000    Accuracy(macro-avg): 0.980000    Log-loss: 0.061647    Log-loss reduction: 0.943887      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.980000 (0.0000)    Accuracy(macro-avg): 0.980000 (0.0000)    Log-loss: 0.061647 (0.0000)    Log-loss reduction: 0.943887 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 0.007      Suffix of length 27 compared against sequence of length 71  Test EnsemblesMultiAveragerSDCATest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.OneClassSvmLibsvmWrapperTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestPredictors.RegressorLightGBMTest [SKIP]  LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNonNegativeTest with memory usage 155,107,328.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNonNegativeTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomPartitionInstanceSelectorTest  Running 'LogisticRegression' on 'breast-cancer'  Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt} norm=no  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Warning:  Skipped 16 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 228 | 11 | 0.9540    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9580 | 0.9753 |    OVERALL 0/1 ACCURACY: 0.969253    LOG LOSS/instance: 0.109007    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.883291    AUC: 0.996287      OVERALL RESULTS    ---------------------------------------    AUC: 0.996287 (0.0000)    Accuracy: 0.969253 (0.0000)    Positive precision: 0.957983 (0.0000)    Positive recall: 0.953975 (0.0000)    Negative precision: 0.975281 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.109007 (0.0000)    Log-loss reduction: 0.883291 (0.0000)    F1 Score: 0.955975 (0.0000)    AUPRC: 0.992293 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 0.012      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-non-negative-TrainTest-breast-cancer.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 228 | 11 | 0.9540    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9580 | 0.9753 |    OVERALL 0/1 ACCURACY: 0.969253    LOG LOSS/instance: 0.109007    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.883291    AUC: 0.996287      OVERALL RESULTS    ---------------------------------------    AUC: 0.996287 (0.0000)    Accuracy: 0.969253 (0.0000)    Positive precision: 0.957983 (0.0000)    Positive recall: 0.953975 (0.0000)    Negative precision: 0.975281 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.109007 (0.0000)    Log-loss reduction: 0.883291 (0.0000)    F1 Score: 0.955975 (0.0000)    AUPRC: 0.992293 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 0.006      Suffix of length 34 compared against sequence of length 42  Running 'LogisticRegression' on 'breast-cancer'  Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt} norm=no threads-  maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} threads=- norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Not adding a normalizer.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 9 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 129 | 5 | 0.9627    negative || 8 | 212 | 0.9636    ||======================    Precision || 0.9416 | 0.9770 |    OVERALL 0/1 ACCURACY: 0.963277    LOG LOSS/instance: 0.140964    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.852702    AUC: 0.994437    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 96 | 9 | 0.9143    negative || 3 | 221 | 0.9866    ||======================    Precision || 0.9697 | 0.9609 |    OVERALL 0/1 ACCURACY: 0.963526    LOG LOSS/instance: 0.111876    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.876169    AUC: 0.997066      OVERALL RESULTS    ---------------------------------------    AUC: 0.995752 (0.0013)    Accuracy: 0.963401 (0.0001)    Positive precision: 0.955651 (0.0140)    Positive recall: 0.938486 (0.0242)    Negative precision: 0.968914 (0.0080)    Negative recall: 0.975122 (0.0115)    Log-loss: 0.126420 (0.0145)    Log-loss reduction: 0.864435 (0.0117)    F1 Score: 0.946603 (0.0054)    AUPRC: 0.991761 (0.0020)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 0.015      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-non-negative-CV-breast-cancer-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/netcoreapp/LogisticRegression-non-negative-CV-breast-cancer-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/osx-arm64/LogisticRegression-non-negative-CV-breast-cancer.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-non-negative-CV-breast-cancer.txt'  Test BinaryClassifierLogisticRegressionNonNegativeTest: completed normally: passed  Test BinaryClassifierLogisticRegressionNonNegativeTest is using osx-arm64 configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomPartitionInstanceSelectorTest with memory usage 155,975,680.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomPartitionInstanceSelectorTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationNoOpGroupIdTest  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{nm=5 st=RandomPartitionSelector tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{nm=5 st=RandomPartitionSelector tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 5 learners for the batch 1    Beginning training model 1 of 5    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter)    Trainer 1 of 5 finished in 00:00:00.0001687    Beginning training model 2 of 5    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 5 instances with missing features during training (over 1 iterations; 5 inst/iter)    Trainer 2 of 5 finished in 00:00:00.0001215    Beginning training model 3 of 5    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 3 instances with missing features during training (over 1 iterations; 3 inst/iter)    Trainer 3 of 5 finished in 00:00:00.0001199    Beginning training model 4 of 5    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 1 instances with missing features during training (over 1 iterations; 1 inst/iter)    Trainer 4 of 5 finished in 00:00:00.0001123    Beginning training model 5 of 5    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 3 instances with missing features during training (over 1 iterations; 3 inst/iter)    Trainer 5 of 5 finished in 00:00:00.0001063    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 235 | 4 | 0.9833    negative || 13 | 431 | 0.9707    ||======================    Precision || 0.9476 | 0.9908 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.126392    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.864677    AUC: 0.995453      OVERALL RESULTS    ---------------------------------------    AUC: 0.995453 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.947581 (0.0000)    Positive recall: 0.983264 (0.0000)    Negative precision: 0.990805 (0.0000)    Negative recall: 0.970721 (0.0000)    Log-loss: 0.126392 (0.0000)    Log-loss reduction: 0.864677 (0.0000)    F1 Score: 0.965092 (0.0000)    AUPRC: 0.990701 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 0.021      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomPartition-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 235 | 4 | 0.9833    negative || 13 | 431 | 0.9707    ||======================    Precision || 0.9476 | 0.9908 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.126392    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.864677    AUC: 0.995453      OVERALL RESULTS    ---------------------------------------    AUC: 0.995453 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.947581 (0.0000)    Positive recall: 0.983264 (0.0000)    Negative precision: 0.990805 (0.0000)    Negative recall: 0.970721 (0.0000)    Log-loss: 0.126392 (0.0000)    Log-loss reduction: 0.864677 (0.0000)    F1 Score: 0.965092 (0.0000)    AUPRC: 0.990701 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 0.01      Suffix of length 34 compared against sequence of length 58  Test EnsemblesRandomPartitionInstanceSelectorTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationNoOpGroupIdTest with memory usage 156,221,440.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationNoOpGroupIdTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierPerceptronTest  Running 'FastTreeBinaryClassification' on 'breast-cancer-group'  Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt loader=Text{col=Label:0 col=GroupId:U4[0-10]:1 col=Features:1-*} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group.txt loader=Text{col=Label:0 col=GroupId:U4[0-10]:1 col=Features:1-*} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  This is not ranking problem, Group Id 'GroupId' column will be ignored    Warning:  Skipped 16 instances with missing features during training    Processed 683 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Warning:  This is not ranking problem, Group Id 'GroupId' column will be ignored    Warning:  Skipped 16 instances with missing features during training    Reserved memory for tree learner: 3852 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 240 | 1 | 0.9959    negative || 13 | 445 | 0.9716    ||======================    Precision || 0.9486 | 0.9978 |    OVERALL 0/1 ACCURACY: 0.979971    LOG LOSS/instance: 0.092572    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.900387    AUC: 0.995370      OVERALL RESULTS    ---------------------------------------    AUC: 0.995370 (0.0000)    Accuracy: 0.979971 (0.0000)    Positive precision: 0.948617 (0.0000)    Positive recall: 0.995851 (0.0000)    Negative precision: 0.997758 (0.0000)    Negative recall: 0.971616 (0.0000)    Log-loss: 0.092572 (0.0000)    Log-loss reduction: 0.900387 (0.0000)    F1 Score: 0.971660 (0.0000)    AUPRC: 0.970606 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 0.041      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-breast-cancer-group-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 240 | 1 | 0.9959    negative || 13 | 445 | 0.9716    ||======================    Precision || 0.9486 | 0.9978 |    OVERALL 0/1 ACCURACY: 0.979971    LOG LOSS/instance: 0.092572    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.900387    AUC: 0.995370      OVERALL RESULTS    ---------------------------------------    AUC: 0.995370 (0.0000)    Accuracy: 0.979971 (0.0000)    Positive precision: 0.948617 (0.0000)    Positive recall: 0.995851 (0.0000)    Negative precision: 0.997758 (0.0000)    Negative recall: 0.971616 (0.0000)    Log-loss: 0.092572 (0.0000)    Log-loss reduction: 0.900387 (0.0000)    F1 Score: 0.971660 (0.0000)    AUPRC: 0.970606 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 0.008      Suffix of length 33 compared against sequence of length 47  Test FastTreeBinaryClassificationNoOpGroupIdTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.LightGBMClassificationTest [SKIP]  LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierPerceptronTest with memory usage 156,286,976.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierPerceptronTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.TestEnsembleCombiner  Running 'AveragedPerceptron' on 'breast-cancer'  Running as: TrainTest tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter)    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 7 | 0.9707    negative || 12 | 432 | 0.9730    ||======================    Precision || 0.9508 | 0.9841 |    OVERALL 0/1 ACCURACY: 0.972182    LOG LOSS/instance: 0.115962    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.875844    AUC: 0.995995      OVERALL RESULTS    ---------------------------------------    AUC: 0.995995 (0.0000)    Accuracy: 0.972182 (0.0000)    Positive precision: 0.950820 (0.0000)    Positive recall: 0.970711 (0.0000)    Negative precision: 0.984055 (0.0000)    Negative recall: 0.972973 (0.0000)    Log-loss: 0.115962 (0.0000)    Log-loss reduction: 0.875844 (0.0000)    F1 Score: 0.960663 (0.0000)    AUPRC: 0.991840 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 0.02      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer-out.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 7 | 0.9707    negative || 12 | 432 | 0.9730    ||======================    Precision || 0.9508 | 0.9841 |    OVERALL 0/1 ACCURACY: 0.972182    LOG LOSS/instance: 0.115962    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.875844    AUC: 0.995995      OVERALL RESULTS    ---------------------------------------    AUC: 0.995995 (0.0000)    Accuracy: 0.972182 (0.0000)    Positive precision: 0.950820 (0.0000)    Positive recall: 0.970711 (0.0000)    Negative precision: 0.984055 (0.0000)    Negative recall: 0.972973 (0.0000)    Log-loss: 0.115962 (0.0000)    Log-loss reduction: 0.875844 (0.0000)    F1 Score: 0.960663 (0.0000)    AUPRC: 0.991840 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:17 PM Time elapsed(s): 0.006      Suffix of length 34 compared against sequence of length 38  Running 'AveragedPerceptron' on 'breast-cancer'  Running as: CV tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt} threads-  maml.exe CV tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)    Training calibrator.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 132 | 2 | 0.9851    negative || 8 | 212 | 0.9636    ||======================    Precision || 0.9429 | 0.9907 |    OVERALL 0/1 ACCURACY: 0.971751    LOG LOSS/instance: 0.136411    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.857460    AUC: 0.994199    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 98 | 7 | 0.9333    negative || 3 | 221 | 0.9866    ||======================    Precision || 0.9703 | 0.9693 |    OVERALL 0/1 ACCURACY: 0.969605    LOG LOSS/instance: 0.118826    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.868476    AUC: 0.997577      OVERALL RESULTS    ---------------------------------------    AUC: 0.995888 (0.0017)    Accuracy: 0.970678 (0.0011)    Positive precision: 0.956577 (0.0137)    Positive recall: 0.959204 (0.0259)    Negative precision: 0.979976 (0.0107)    Negative recall: 0.975122 (0.0115)    Log-loss: 0.127618 (0.0088)    Log-loss reduction: 0.862968 (0.0055)    F1 Score: 0.957480 (0.0060)    AUPRC: 0.992003 (0.0026)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.02      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer-out.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer-rp.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt  Output matches baseline: 'AveragedPerceptron/AveragedPerceptron-CV-breast-cancer.txt'  Test BinaryClassifierPerceptronTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.TestEnsembleCombiner with memory usage 155,041,792.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.TestEnsembleCombiner [PASS]  Output:  Test TestEnsembleCombiner: completed normally: passed Starting test: Microsoft.ML.RunTests.TestPredictors.FastForestClassificationTest Finished test: Microsoft.ML.RunTests.TestPredictors.FastForestClassificationTest with memory usage 155,025,408.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.FastForestClassificationTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesDefaultTest  Running 'FastForestClassification' on 'breast-cancer'  Running as: TrainTest tr=FastForestClassification{nl=5 mil=10 iter=10} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=FastForestClassification{nl=5 mil=10 iter=10} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Processed 683 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Reserved memory for tree learner: 3852 bytes    Starting to train ...    Training calibrator.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 234 | 7 | 0.9710    negative || 16 | 442 | 0.9651    ||======================    Precision || 0.9360 | 0.9844 |    OVERALL 0/1 ACCURACY: 0.967096    LOG LOSS/instance: 0.162280    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.825377    AUC: 0.987892      OVERALL RESULTS    ---------------------------------------    AUC: 0.987892 (0.0000)    Accuracy: 0.967096 (0.0000)    Positive precision: 0.936000 (0.0000)    Positive recall: 0.970954 (0.0000)    Negative precision: 0.984410 (0.0000)    Negative recall: 0.965066 (0.0000)    Log-loss: 0.162280 (0.0000)    Log-loss reduction: 0.825377 (0.0000)    F1 Score: 0.953157 (0.0000)    AUPRC: 0.957347 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.031      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestClassification/FastForestClassification-TrainTest-breast-cancer-out.txt  Output matches baseline: 'FastForestClassification/FastForestClassification-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestClassification/FastForestClassification-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'FastForestClassification/FastForestClassification-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestClassification/FastForestClassification-TrainTest-breast-cancer.txt  Output matches baseline: 'FastForestClassification/FastForestClassification-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 234 | 7 | 0.9710    negative || 16 | 442 | 0.9651    ||======================    Precision || 0.9360 | 0.9844 |    OVERALL 0/1 ACCURACY: 0.967096    LOG LOSS/instance: 0.162280    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.825377    AUC: 0.987892      OVERALL RESULTS    ---------------------------------------    AUC: 0.987892 (0.0000)    Accuracy: 0.967096 (0.0000)    Positive precision: 0.936000 (0.0000)    Positive recall: 0.970954 (0.0000)    Negative precision: 0.984410 (0.0000)    Negative recall: 0.965066 (0.0000)    Log-loss: 0.162280 (0.0000)    Log-loss reduction: 0.825377 (0.0000)    F1 Score: 0.953157 (0.0000)    AUPRC: 0.957347 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.012      Suffix of length 33 compared against sequence of length 45  Running 'FastForestClassification' on 'breast-cancer'  Running as: CV tr=FastForestClassification{nl=5 mil=10 iter=10} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-CV-breast-cancer.txt} threads-  maml.exe CV tr=FastForestClassification{nl=5 mil=10 iter=10} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  Skipped 8 instances with missing features during training    Processed 329 instances    Binning and forming Feature objects    Reserved memory for tree learner: 3852 bytes    Starting to train ...    Training calibrator.    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  Skipped 8 instances with missing features during training    Processed 354 instances    Binning and forming Feature objects    Reserved memory for tree learner: 3816 bytes    Starting to train ...    Training calibrator.    TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 127 | 7 | 0.9478    negative || 13 | 215 | 0.9430    ||======================    Precision || 0.9071 | 0.9685 |    OVERALL 0/1 ACCURACY: 0.944751    LOG LOSS/instance: 0.237138    Test-set entropy (prior Log-Loss/instance): 0.950799    LOG-LOSS REDUCTION (RIG): 0.750591    AUC: 0.980312    TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 100 | 7 | 0.9346    negative || 7 | 223 | 0.9696    ||======================    Precision || 0.9346 | 0.9696 |    OVERALL 0/1 ACCURACY: 0.958457    LOG LOSS/instance: 0.153923    Test-set entropy (prior Log-Loss/instance): 0.901650    LOG-LOSS REDUCTION (RIG): 0.829288    AUC: 0.993722      OVERALL RESULTS    ---------------------------------------    AUC: 0.987017 (0.0067)    Accuracy: 0.951604 (0.0069)    Positive precision: 0.920861 (0.0137)    Positive recall: 0.941170 (0.0066)    Negative precision: 0.969017 (0.0005)    Negative recall: 0.956274 (0.0133)    Log-loss: 0.195530 (0.0416)    Log-loss reduction: 0.789939 (0.0393)    F1 Score: 0.930793 (0.0038)    AUPRC: 0.961717 (0.0240)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.038      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestClassification/FastForestClassification-CV-breast-cancer-out.txt  Output matches baseline: 'FastForestClassification/FastForestClassification-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestClassification/FastForestClassification-CV-breast-cancer-rp.txt  Output matches baseline: 'FastForestClassification/FastForestClassification-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastForestClassification/FastForestClassification-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastForestClassification/FastForestClassification-CV-breast-cancer.txt  Output matches baseline: 'FastForestClassification/FastForestClassification-CV-breast-cancer.txt'  Test FastForestClassificationTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesDefaultTest with memory usage 155,074,560.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesDefaultTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiVotingCombinerTest  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{nm=20 tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{nm=20 tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 20 learners for the batch 1    Beginning training model 1 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter)    Trainer 1 of 20 finished in 00:00:00.0007488    Beginning training model 2 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 2 of 20 finished in 00:00:00.0004975    Beginning training model 3 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter)    Trainer 3 of 20 finished in 00:00:00.0002800    Beginning training model 4 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 4 of 20 finished in 00:00:00.0032465    Beginning training model 5 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 5 of 20 finished in 00:00:00.0003150    Beginning training model 6 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 6 of 20 finished in 00:00:00.0002853    Beginning training model 7 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 7 of 20 finished in 00:00:00.0002744    Beginning training model 8 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 8 of 20 finished in 00:00:00.0002691    Beginning training model 9 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 9 of 20 finished in 00:00:00.0002904    Beginning training model 10 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 10 of 20 finished in 00:00:00.0002710    Beginning training model 11 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 11 of 20 finished in 00:00:00.0002720    Beginning training model 12 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)    Trainer 12 of 20 finished in 00:00:00.0003115    Beginning training model 13 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 13 of 20 finished in 00:00:00.0002982    Beginning training model 14 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter)    Trainer 14 of 20 finished in 00:00:00.0003001    Beginning training model 15 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 15 of 20 finished in 00:00:00.0003195    Beginning training model 16 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 16 of 20 finished in 00:00:00.0003062    Beginning training model 17 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 17 of 20 finished in 00:00:00.0002966    Beginning training model 18 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter)    Trainer 18 of 20 finished in 00:00:00.0002974    Beginning training model 19 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter)    Trainer 19 of 20 finished in 00:00:00.0002910    Beginning training model 20 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 20 of 20 finished in 00:00:00.0003067    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9585 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.115894    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.875917    AUC: 0.995976      OVERALL RESULTS    ---------------------------------------    AUC: 0.995976 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.958506 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981900 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.115894 (0.0000)    Log-loss reduction: 0.875917 (0.0000)    F1 Score: 0.962500 (0.0000)    AUPRC: 0.991794 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.033      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-Default-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-Default-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-Default-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9585 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.115894    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.875917    AUC: 0.995976      OVERALL RESULTS    ---------------------------------------    AUC: 0.995976 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.958506 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981900 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.115894 (0.0000)    Log-loss reduction: 0.875917 (0.0000)    F1 Score: 0.962500 (0.0000)    AUPRC: 0.991794 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.015      Suffix of length 34 compared against sequence of length 118  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: CV tr=WeightedEnsemble{nm=20 tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-CV-breast-cancer.txt} threads- loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe CV tr=WeightedEnsemble{nm=20 tp=-} threads=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-CV-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 20 learners for the batch 1    Beginning training model 1 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter)    Trainer 1 of 20 finished in 00:00:00.0008262    Beginning training model 2 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter)    Trainer 2 of 20 finished in 00:00:00.0002049    Beginning training model 3 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 3 of 20 finished in 00:00:00.0002107    Beginning training model 4 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter)    Trainer 4 of 20 finished in 00:00:00.0001902    Beginning training model 5 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter)    Trainer 5 of 20 finished in 00:00:00.0002022    Beginning training model 6 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter)    Trainer 6 of 20 finished in 00:00:00.0001952    Beginning training model 7 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter)    Trainer 7 of 20 finished in 00:00:00.0001752    Beginning training model 8 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter)    Trainer 8 of 20 finished in 00:00:00.0001895    Beginning training model 9 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter)    Trainer 9 of 20 finished in 00:00:00.0001770    Beginning training model 10 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter)    Trainer 10 of 20 finished in 00:00:00.0001802    Beginning training model 11 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter)    Trainer 11 of 20 finished in 00:00:00.0001703    Beginning training model 12 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter)    Trainer 12 of 20 finished in 00:00:00.0001846    Beginning training model 13 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter)    Trainer 13 of 20 finished in 00:00:00.0001802    Beginning training model 14 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter)    Trainer 14 of 20 finished in 00:00:00.0001700    Beginning training model 15 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter)    Trainer 15 of 20 finished in 00:00:00.0001640    Beginning training model 16 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter)    Trainer 16 of 20 finished in 00:00:00.0001716    Beginning training model 17 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter)    Trainer 17 of 20 finished in 00:00:00.0001684    Beginning training model 18 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter)    Trainer 18 of 20 finished in 00:00:00.0001781    Beginning training model 19 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter)    Trainer 19 of 20 finished in 00:00:00.0001827    Beginning training model 20 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter)    Trainer 20 of 20 finished in 00:00:00.0001702    Training calibrator.    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 20 learners for the batch 1    Beginning training model 1 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)    Trainer 1 of 20 finished in 00:00:00.0011516    Beginning training model 2 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter)    Trainer 2 of 20 finished in 00:00:00.0001906    Beginning training model 3 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter)    Trainer 3 of 20 finished in 00:00:00.0001762    Beginning training model 4 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter)    Trainer 4 of 20 finished in 00:00:00.0001753    Beginning training model 5 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter)    Trainer 5 of 20 finished in 00:00:00.0001821    Beginning training model 6 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter)    Trainer 6 of 20 finished in 00:00:00.0001668    Beginning training model 7 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter)    Trainer 7 of 20 finished in 00:00:00.0001809    Beginning training model 8 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 8 of 20 finished in 00:00:00.0001837    Beginning training model 9 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter)    Trainer 9 of 20 finished in 00:00:00.0001669    Beginning training model 10 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter)    Trainer 10 of 20 finished in 00:00:00.0001725    Beginning training model 11 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter)    Trainer 11 of 20 finished in 00:00:00.0001820    Beginning training model 12 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter)    Trainer 12 of 20 finished in 00:00:00.0001807    Beginning training model 13 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter)    Trainer 13 of 20 finished in 00:00:00.0001813    Beginning training model 14 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter)    Trainer 14 of 20 finished in 00:00:00.0001859    Beginning training model 15 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter)    Trainer 15 of 20 finished in 00:00:00.0001850    Beginning training model 16 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)    Trainer 16 of 20 finished in 00:00:00.0001825    Beginning training model 17 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter)    Trainer 17 of 20 finished in 00:00:00.0018915    Beginning training model 18 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 5 instances with missing features during training (over 1 iterations; 5 inst/iter)    Trainer 18 of 20 finished in 00:00:00.0001861    Beginning training model 19 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter)    Trainer 19 of 20 finished in 00:00:00.0001971    Beginning training model 20 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter)    Trainer 20 of 20 finished in 00:00:00.0001918    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 129 | 5 | 0.9627    negative || 7 | 213 | 0.9682    ||======================    Precision || 0.9485 | 0.9771 |    OVERALL 0/1 ACCURACY: 0.966102    LOG LOSS/instance: 0.143167    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.850400    AUC: 0.993996    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 103 | 2 | 0.9810    negative || 4 | 220 | 0.9821    ||======================    Precision || 0.9626 | 0.9910 |    OVERALL 0/1 ACCURACY: 0.981763    LOG LOSS/instance: 0.115437    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.872227    AUC: 0.997832      OVERALL RESULTS    ---------------------------------------    AUC: 0.995914 (0.0019)    Accuracy: 0.973932 (0.0078)    Positive precision: 0.955573 (0.0070)    Positive recall: 0.971819 (0.0091)    Negative precision: 0.984028 (0.0070)    Negative recall: 0.975162 (0.0070)    Log-loss: 0.129302 (0.0139)    Log-loss reduction: 0.861313 (0.0109)    F1 Score: 0.963627 (0.0081)    AUPRC: 0.992159 (0.0031)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.034      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-CV-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-Default-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Default-CV-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-Default-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Default-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/netcoreapp/WE-Default-CV-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-Default-CV-breast-cancer.txt'  Test EnsemblesDefaultTest: completed normally: passed  Test EnsemblesDefaultTest is using netcoreapp configuration specific baselines.  Microsoft.ML.RunTests.TestPredictors.FastTreeRegressionCategoricalSplitTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiVotingCombinerTest with memory usage 155,074,560.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiVotingCombinerTest [PASS]  Output:  Running 'WeightedEnsembleMulticlass' on 'iris'  Running as: TrainTest tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiVoting tp=-} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt xf=Term{col=Label} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiVoting tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-model.zip seed=1 xf=Term{col=Label} Starting test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationCategoricalSplitTest    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 5 learners for the batch 1    Beginning training model 1 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 1 of 5 finished in 00:00:00.0103462    Beginning training model 2 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 2 of 5 finished in 00:00:00.0106555    Beginning training model 3 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 3 of 5 finished in 00:00:00.0078666    Beginning training model 4 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 10 of 15 weights.    Trainer 4 of 5 finished in 00:00:00.0094743    Beginning training model 5 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 5 of 5 finished in 00:00:00.0090179    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 44 | 6 | 0.8800    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9565 |0.8889 |    Accuracy(micro-avg): 0.946667    Accuracy(macro-avg): 0.946667    Log-loss: 0.511576    Log-loss reduction: 0.534344      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.946667 (0.0000)    Accuracy(macro-avg): 0.946667 (0.0000)    Log-loss: 0.511576 (0.0000)    Log-loss reduction: 0.534344 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.061      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Voting-TrainTest-iris-out.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-rp.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Voting-TrainTest-iris-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 44 | 6 | 0.8800    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9565 |0.8889 |    Accuracy(micro-avg): 0.946667    Accuracy(macro-avg): 0.946667    Log-loss: 0.511576    Log-loss reduction: 0.534344      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.946667 (0.0000)    Accuracy(macro-avg): 0.946667 (0.0000)    Log-loss: 0.511576 (0.0000)    Log-loss reduction: 0.534344 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.008      Suffix of length 27 compared against sequence of length 61  Test EnsemblesMultiVotingCombinerTest: completed normally: passed  Test EnsemblesMultiVotingCombinerTest is using osx-arm64 configuration specific baselines.  Microsoft.ML.RunTests.TestPredictors.WeightingClassificationFastRankPredictorsTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationCategoricalSplitTest with memory usage 158,187,520.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationCategoricalSplitTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.NoCalibratorLinearSvmTest  Running 'FastTreeBinaryClassification' on 'Census-Cat-Only'  Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Cat} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 500 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Reserved memory for tree learner: 3180 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 55 | 60 | 0.4783    negative || 17 | 368 | 0.9558    ||======================    Precision || 0.7639 | 0.8598 |    OVERALL 0/1 ACCURACY: 0.846000    LOG LOSS/instance: 0.481805    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.380722    AUC: 0.893281      OVERALL RESULTS    ---------------------------------------    AUC: 0.893281 (0.0000)    Accuracy: 0.846000 (0.0000)    Positive precision: 0.763889 (0.0000)    Positive recall: 0.478261 (0.0000)    Negative precision: 0.859813 (0.0000)    Negative recall: 0.955844 (0.0000)    Log-loss: 0.481805 (0.0000)    Log-loss reduction: 0.380722 (0.0000)    F1 Score: 0.588235 (0.0000)    AUPRC: 0.738040 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.053      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt'  Saving summary with: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt}  Saving ini file: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt} ini={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat.ini}  Saving predictor summary    Saving predictor as ini    Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat.ini and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat.ini  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat.ini'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat.txt data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census-Cat-Only.Cat-model.zip seed=1    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 55 | 60 | 0.4783    negative || 17 | 368 | 0.9558    ||======================    Precision || 0.7639 | 0.8598 |    OVERALL 0/1 ACCURACY: 0.846000    LOG LOSS/instance: 0.481805    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.380722    AUC: 0.893281      OVERALL RESULTS    ---------------------------------------    AUC: 0.893281 (0.0000)    Accuracy: 0.846000 (0.0000)    Positive precision: 0.763889 (0.0000)    Positive recall: 0.478261 (0.0000)    Negative precision: 0.859813 (0.0000)    Negative recall: 0.955844 (0.0000)    Log-loss: 0.481805 (0.0000)    Log-loss reduction: 0.380722 (0.0000)    F1 Score: 0.588235 (0.0000)    AUPRC: 0.738040 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.008      Suffix of length 33 compared against sequence of length 43  Running 'FastTreeBinaryClassification' on 'Census'  Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat}    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 500 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Reserved memory for tree learner: 18324 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 75 | 40 | 0.6522    negative || 6 | 379 | 0.9844    ||======================    Precision || 0.9259 | 0.9045 |    OVERALL 0/1 ACCURACY: 0.908000    LOG LOSS/instance: 0.353688    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.545395    AUC: 0.958893      OVERALL RESULTS    ---------------------------------------    AUC: 0.958893 (0.0000)    Accuracy: 0.908000 (0.0000)    Positive precision: 0.925926 (0.0000)    Positive recall: 0.652174 (0.0000)    Negative precision: 0.904535 (0.0000)    Negative recall: 0.984416 (0.0000)    Log-loss: 0.353688 (0.0000)    Log-loss reduction: 0.545395 (0.0000)    F1 Score: 0.765306 (0.0000)    AUPRC: 0.895540 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.042      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-out.txt'  Saving summary with: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-summary.txt}  Saving ini file: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-summary.txt} ini={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat.ini}  Saving predictor summary    Saving predictor as ini    Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-summary.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-summary.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-summary.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat.ini and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat.ini  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat.ini'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat.txt data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTree-TrainTest-Census.Cat-model.zip seed=1    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 75 | 40 | 0.6522    negative || 6 | 379 | 0.9844    ||======================    Precision || 0.9259 | 0.9045 |    OVERALL 0/1 ACCURACY: 0.908000    LOG LOSS/instance: 0.353688    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.545395    AUC: 0.958893      OVERALL RESULTS    ---------------------------------------    AUC: 0.958893 (0.0000)    Accuracy: 0.908000 (0.0000)    Positive precision: 0.925926 (0.0000)    Positive recall: 0.652174 (0.0000)    Negative precision: 0.904535 (0.0000)    Negative recall: 0.984416 (0.0000)    Log-loss: 0.353688 (0.0000)    Log-loss reduction: 0.545395 (0.0000)    F1 Score: 0.765306 (0.0000)    AUPRC: 0.895540 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.008      Suffix of length 33 compared against sequence of length 43  Running 'FastTreeBinaryClassification' on 'Census-Cat-Only'  Running as: TrainTest tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Cat} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 500 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Reserved memory for tree learner: 3180 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 67 | 48 | 0.5826    negative || 18 | 367 | 0.9532    ||======================    Precision || 0.7882 | 0.8843 |    OVERALL 0/1 ACCURACY: 0.868000    LOG LOSS/instance: 0.439868    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.434625    AUC: 0.916804      OVERALL RESULTS    ---------------------------------------    AUC: 0.916804 (0.0000)    Accuracy: 0.868000 (0.0000)    Positive precision: 0.788235 (0.0000)    Positive recall: 0.582609 (0.0000)    Negative precision: 0.884337 (0.0000)    Negative recall: 0.953247 (0.0000)    Log-loss: 0.439868 (0.0000)    Log-loss reduction: 0.434625 (0.0000)    F1 Score: 0.670000 (0.0000)    AUPRC: 0.770221 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.038      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-out.txt'  Saving summary with: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt}  Saving ini file: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt} ini={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini}  Saving predictor summary    Saving predictor as ini    Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-model.zip seed=1    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 67 | 48 | 0.5826    negative || 18 | 367 | 0.9532    ||======================    Precision || 0.7882 | 0.8843 |    OVERALL 0/1 ACCURACY: 0.868000    LOG LOSS/instance: 0.439868    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.434625    AUC: 0.916804      OVERALL RESULTS    ---------------------------------------    AUC: 0.916804 (0.0000)    Accuracy: 0.868000 (0.0000)    Positive precision: 0.788235 (0.0000)    Positive recall: 0.582609 (0.0000)    Negative precision: 0.884337 (0.0000)    Negative recall: 0.953247 (0.0000)    Log-loss: 0.439868 (0.0000)    Log-loss reduction: 0.434625 (0.0000)    F1 Score: 0.670000 (0.0000)    AUPRC: 0.770221 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.008      Suffix of length 33 compared against sequence of length 43  Running 'FastTreeBinaryClassification' on 'Census'  Running as: TrainTest tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat}    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 500 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise    Reserved memory for tree learner: 18324 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 83 | 32 | 0.7217    negative || 11 | 374 | 0.9714    ||======================    Precision || 0.8830 | 0.9212 |    OVERALL 0/1 ACCURACY: 0.914000    LOG LOSS/instance: 0.327460    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.579106    AUC: 0.965037      OVERALL RESULTS    ---------------------------------------    AUC: 0.965037 (0.0000)    Accuracy: 0.914000 (0.0000)    Positive precision: 0.882979 (0.0000)    Positive recall: 0.721739 (0.0000)    Negative precision: 0.921182 (0.0000)    Negative recall: 0.971429 (0.0000)    Log-loss: 0.327460 (0.0000)    Log-loss reduction: 0.579106 (0.0000)    F1 Score: 0.794258 (0.0000)    AUPRC: 0.907541 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.039      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-out.txt'  Saving summary with: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-summary.txt}  Saving ini file: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-summary.txt} ini={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat.ini}  Saving predictor summary    Saving predictor as ini    Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-summary.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-summary.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-summary.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat.ini and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat.ini  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat.ini'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat.txt data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategorical-TrainTest-Census.Cat-model.zip seed=1    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 83 | 32 | 0.7217    negative || 11 | 374 | 0.9714    ||======================    Precision || 0.8830 | 0.9212 |    OVERALL 0/1 ACCURACY: 0.914000    LOG LOSS/instance: 0.327460    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.579106    AUC: 0.965037      OVERALL RESULTS    ---------------------------------------    AUC: 0.965037 (0.0000)    Accuracy: 0.914000 (0.0000)    Positive precision: 0.882979 (0.0000)    Positive recall: 0.721739 (0.0000)    Negative precision: 0.921182 (0.0000)    Negative recall: 0.971429 (0.0000)    Log-loss: 0.327460 (0.0000)    Log-loss reduction: 0.579106 (0.0000)    F1 Score: 0.794258 (0.0000)    AUPRC: 0.907541 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.008      Suffix of length 33 compared against sequence of length 43  Running 'FastTreeBinaryClassification' on 'Census-Cat-Only'  Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Cat} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise on disk    Processed 500 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise on disk    Reserved memory for tree learner: 11232 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 55 | 60 | 0.4783    negative || 17 | 368 | 0.9558    ||======================    Precision || 0.7639 | 0.8598 |    OVERALL 0/1 ACCURACY: 0.846000    LOG LOSS/instance: 0.481805    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.380722    AUC: 0.893281      OVERALL RESULTS    ---------------------------------------    AUC: 0.893281 (0.0000)    Accuracy: 0.846000 (0.0000)    Positive precision: 0.763889 (0.0000)    Positive recall: 0.478261 (0.0000)    Negative precision: 0.859813 (0.0000)    Negative recall: 0.955844 (0.0000)    Log-loss: 0.481805 (0.0000)    Log-loss reduction: 0.380722 (0.0000)    F1 Score: 0.588235 (0.0000)    AUPRC: 0.738040 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.101      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-out.txt'  Saving summary with: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt}  Saving ini file: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt} ini={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini}  Saving predictor summary    Saving predictor as ini    Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-model.zip seed=1    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 55 | 60 | 0.4783    negative || 17 | 368 | 0.9558    ||======================    Precision || 0.7639 | 0.8598 |    OVERALL 0/1 ACCURACY: 0.846000    LOG LOSS/instance: 0.481805    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.380722    AUC: 0.893281      OVERALL RESULTS    ---------------------------------------    AUC: 0.893281 (0.0000)    Accuracy: 0.846000 (0.0000)    Positive precision: 0.763889 (0.0000)    Positive recall: 0.478261 (0.0000)    Negative precision: 0.859813 (0.0000)    Negative recall: 0.955844 (0.0000)    Log-loss: 0.481805 (0.0000)    Log-loss reduction: 0.380722 (0.0000)    F1 Score: 0.588235 (0.0000)    AUPRC: 0.738040 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.007      Suffix of length 33 compared against sequence of length 43  Running 'FastTreeBinaryClassification' on 'Census'  Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat}    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise on disk    Processed 500 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise on disk    Reserved memory for tree learner: 26424 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 75 | 40 | 0.6522    negative || 6 | 379 | 0.9844    ||======================    Precision || 0.9259 | 0.9045 |    OVERALL 0/1 ACCURACY: 0.908000    LOG LOSS/instance: 0.353688    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.545395    AUC: 0.958893      OVERALL RESULTS    ---------------------------------------    AUC: 0.958893 (0.0000)    Accuracy: 0.908000 (0.0000)    Positive precision: 0.925926 (0.0000)    Positive recall: 0.652174 (0.0000)    Negative precision: 0.904535 (0.0000)    Negative recall: 0.984416 (0.0000)    Log-loss: 0.353688 (0.0000)    Log-loss reduction: 0.545395 (0.0000)    F1 Score: 0.765306 (0.0000)    AUPRC: 0.895540 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:18 PM Time elapsed(s): 0.098      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-out.txt'  Saving summary with: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-summary.txt}  Saving ini file: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-summary.txt} ini={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat.ini}  Saving predictor summary    Saving predictor as ini    Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-summary.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-summary.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-summary.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat.ini and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat.ini  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat.ini'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat.txt data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeDisk-TrainTest-Census.Cat-model.zip seed=1    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 75 | 40 | 0.6522    negative || 6 | 379 | 0.9844    ||======================    Precision || 0.9259 | 0.9045 |    OVERALL 0/1 ACCURACY: 0.908000    LOG LOSS/instance: 0.353688    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.545395    AUC: 0.958893      OVERALL RESULTS    ---------------------------------------    AUC: 0.958893 (0.0000)    Accuracy: 0.908000 (0.0000)    Positive precision: 0.925926 (0.0000)    Positive recall: 0.652174 (0.0000)    Negative precision: 0.904535 (0.0000)    Negative recall: 0.984416 (0.0000)    Log-loss: 0.353688 (0.0000)    Log-loss reduction: 0.545395 (0.0000)    F1 Score: 0.765306 (0.0000)    AUPRC: 0.895540 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.006      Suffix of length 33 compared against sequence of length 43  Running 'FastTreeBinaryClassification' on 'Census-Cat-Only'  Running as: TrainTest tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Cat} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise on disk    Processed 500 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise on disk    Reserved memory for tree learner: 4152 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 68 | 47 | 0.5913    negative || 16 | 369 | 0.9584    ||======================    Precision || 0.8095 | 0.8870 |    OVERALL 0/1 ACCURACY: 0.874000    LOG LOSS/instance: 0.425411    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.453207    AUC: 0.923817      OVERALL RESULTS    ---------------------------------------    AUC: 0.923817 (0.0000)    Accuracy: 0.874000 (0.0000)    Positive precision: 0.809524 (0.0000)    Positive recall: 0.591304 (0.0000)    Negative precision: 0.887019 (0.0000)    Negative recall: 0.958442 (0.0000)    Log-loss: 0.425411 (0.0000)    Log-loss reduction: 0.453207 (0.0000)    F1 Score: 0.683417 (0.0000)    AUPRC: 0.792176 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.084      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-out.txt'  Saving summary with: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt}  Saving ini file: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt} ini={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini}  Saving predictor summary    Saving predictor as ini    Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-model.zip seed=1    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 68 | 47 | 0.5913    negative || 16 | 369 | 0.9584    ||======================    Precision || 0.8095 | 0.8870 |    OVERALL 0/1 ACCURACY: 0.874000    LOG LOSS/instance: 0.425411    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.453207    AUC: 0.923817      OVERALL RESULTS    ---------------------------------------    AUC: 0.923817 (0.0000)    Accuracy: 0.874000 (0.0000)    Positive precision: 0.809524 (0.0000)    Positive recall: 0.591304 (0.0000)    Negative precision: 0.887019 (0.0000)    Negative recall: 0.958442 (0.0000)    Log-loss: 0.425411 (0.0000)    Log-loss reduction: 0.453207 (0.0000)    F1 Score: 0.683417 (0.0000)    AUPRC: 0.792176 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.007      Suffix of length 33 compared against sequence of length 43  Running 'FastTreeBinaryClassification' on 'Census'  Running as: TrainTest tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat}    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise on disk    Processed 500 instances    Binning and forming Feature objects    Changing data from row-wise to column-wise on disk    Reserved memory for tree learner: 19344 bytes    Starting to train ...    Not training a calibrator because it is not needed.    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 84 | 31 | 0.7304    negative || 10 | 375 | 0.9740    ||======================    Precision || 0.8936 | 0.9236 |    OVERALL 0/1 ACCURACY: 0.918000    LOG LOSS/instance: 0.322363    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.585658    AUC: 0.965624      OVERALL RESULTS    ---------------------------------------    AUC: 0.965624 (0.0000)    Accuracy: 0.918000 (0.0000)    Positive precision: 0.893617 (0.0000)    Positive recall: 0.730435 (0.0000)    Negative precision: 0.923645 (0.0000)    Negative recall: 0.974026 (0.0000)    Log-loss: 0.322363 (0.0000)    Log-loss reduction: 0.585658 (0.0000)    F1 Score: 0.803828 (0.0000)    AUPRC: 0.910384 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.113      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-out.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-out.txt'  Saving summary with: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt}  Saving ini file: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt} ini={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat.ini}  Saving predictor summary    Saving predictor as ini    Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat.ini and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat.ini  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat.ini'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-rp.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat.txt  Output matches baseline: 'FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat.txt data=/tmp/helix/working/99790869/p/test/data/adult.tiny.with-schema.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/FastTreeBinaryClassification/FastTreeCategoricalDisk-TrainTest-Census.Cat-model.zip seed=1    TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 84 | 31 | 0.7304    negative || 10 | 375 | 0.9740    ||======================    Precision || 0.8936 | 0.9236 |    OVERALL 0/1 ACCURACY: 0.918000    LOG LOSS/instance: 0.322363    Test-set entropy (prior Log-Loss/instance): 0.778011    LOG-LOSS REDUCTION (RIG): 0.585658    AUC: 0.965624      OVERALL RESULTS    ---------------------------------------    AUC: 0.965624 (0.0000)    Accuracy: 0.918000 (0.0000)    Positive precision: 0.893617 (0.0000)    Positive recall: 0.730435 (0.0000)    Negative precision: 0.923645 (0.0000)    Negative recall: 0.974026 (0.0000)    Log-loss: 0.322363 (0.0000)    Log-loss reduction: 0.585658 (0.0000)    F1 Score: 0.803828 (0.0000)    AUPRC: 0.910384 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.008      Suffix of length 33 compared against sequence of length 43  Test FastTreeBinaryClassificationCategoricalSplitTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.NoCalibratorLinearSvmTest with memory usage 158,203,904.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.NoCalibratorLinearSvmTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBaseLearnerTest  Running 'LinearSVM' on 'breast-cancer'  Running as: TrainTest tr=LinearSVM{iter=100 lambda=0.03} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt} cali={}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=LinearSVM{iter=100 lambda=0.03} cali={} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter)    Not training a calibrator because a valid calibrator trainer was not provided.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 226 | 13 | 0.9456    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9617 | 0.9710 |    OVERALL 0/1 ACCURACY: 0.967789    LOG LOSS/instance: NaN    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.000000    AUC: 0.995797      OVERALL RESULTS    ---------------------------------------    AUC: 0.995797 (0.0000)    Accuracy: 0.967789 (0.0000)    Positive precision: 0.961702 (0.0000)    Positive recall: 0.945607 (0.0000)    Negative precision: 0.970982 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: NaN (0.0000)    Log-loss reduction: 0.000000 (0.0000)    F1 Score: 0.953586 (0.0000)    AUPRC: 0.991453 (0.0000)      ---------------------------------------    Warning:  Data does not contain a probability column. Will not output the Log-loss column    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.024      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt  Output matches baseline: 'LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt  Output matches baseline: 'LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt  Output matches baseline: 'LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 226 | 13 | 0.9456    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9617 | 0.9710 |    OVERALL 0/1 ACCURACY: 0.967789    LOG LOSS/instance: NaN    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.000000    AUC: 0.995797      OVERALL RESULTS    ---------------------------------------    AUC: 0.995797 (0.0000)    Accuracy: 0.967789 (0.0000)    Positive precision: 0.961702 (0.0000)    Positive recall: 0.945607 (0.0000)    Negative precision: 0.970982 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: NaN (0.0000)    Log-loss reduction: 0.000000 (0.0000)    F1 Score: 0.953586 (0.0000)    AUPRC: 0.991453 (0.0000)      ---------------------------------------    Warning:  Data does not contain a probability column. Will not output the Log-loss column    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.008      Suffix of length 35 compared against sequence of length 39  Running 'LinearSVM' on 'breast-cancer'  Running as: CV tr=LinearSVM{iter=100 lambda=0.03} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt} threads- cali={}  maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali={} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)    Not training a calibrator because a valid calibrator trainer was not provided.    Warning:  Data does not contain a probability column. Will not output the Log-loss column    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Warning:  Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)    Not training a calibrator because a valid calibrator trainer was not provided.    Warning:  Data does not contain a probability column. Will not output the Log-loss column    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 128 | 6 | 0.9552    negative || 7 | 213 | 0.9682    ||======================    Precision || 0.9481 | 0.9726 |    OVERALL 0/1 ACCURACY: 0.963277    LOG LOSS/instance: NaN    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.000000    AUC: 0.994233    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 97 | 8 | 0.9238    negative || 2 | 222 | 0.9911    ||======================    Precision || 0.9798 | 0.9652 |    OVERALL 0/1 ACCURACY: 0.969605    LOG LOSS/instance: NaN    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.000000    AUC: 0.997491      OVERALL RESULTS    ---------------------------------------    AUC: 0.995862 (0.0016)    Accuracy: 0.966441 (0.0032)    Positive precision: 0.963973 (0.0158)    Positive recall: 0.939517 (0.0157)    Negative precision: 0.968910 (0.0037)    Negative recall: 0.979627 (0.0114)    Log-loss: NaN (NaN)    Log-loss reduction: 0.000000 (0.0000)    F1 Score: 0.951327 (0.0003)    AUPRC: 0.991949 (0.0025)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.025      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-out.txt  Output matches baseline: 'LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-rp.txt  Output matches baseline: 'LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt  Output matches baseline: 'LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt'  Test NoCalibratorLinearSvmTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBaseLearnerTest with memory usage 158,203,904.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesBaseLearnerTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionTest  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 3 learners for the batch 1    Beginning training model 1 of 3    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 260 instances with missing features during training (over 10 iterations; 26 inst/iter)    Trainer 1 of 3 finished in 00:00:00.0022481    Beginning training model 2 of 3    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter)    Trainer 2 of 3 finished in 00:00:00.0015477    Beginning training model 3 of 3    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 110 instances with missing features during training (over 10 iterations; 11 inst/iter)    Trainer 3 of 3 finished in 00:00:00.0014955    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9585 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.112168    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.879907    AUC: 0.996240      OVERALL RESULTS    ---------------------------------------    AUC: 0.996240 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.958506 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981900 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.112168 (0.0000)    Log-loss reduction: 0.879907 (0.0000)    F1 Score: 0.962500 (0.0000)    AUPRC: 0.992400 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.026      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-AvgPer-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-AvgPer-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-AvgPer-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-AvgPer-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9585 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.112168    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.879907    AUC: 0.996240      OVERALL RESULTS    ---------------------------------------    AUC: 0.996240 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.958506 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981900 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.112168 (0.0000)    Log-loss reduction: 0.879907 (0.0000)    F1 Score: 0.962500 (0.0000)    AUPRC: 0.992400 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.007      Suffix of length 34 compared against sequence of length 50  Test EnsemblesBaseLearnerTest: completed normally: passed  Test EnsemblesBaseLearnerTest is using osx-arm64 configuration specific baselines.  Microsoft.ML.RunTests.TestPredictors.KMeansClusteringTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionTest with memory usage 158,203,904.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.MulticlassReductionTest  Running 'LogisticRegression' on 'breast-cancer'  Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt} norm=no  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Warning:  Skipped 16 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 228 | 11 | 0.9540    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9580 | 0.9753 |    OVERALL 0/1 ACCURACY: 0.969253    LOG LOSS/instance: 0.111003    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.881154    AUC: 0.996136      OVERALL RESULTS    ---------------------------------------    AUC: 0.996136 (0.0000)    Accuracy: 0.969253 (0.0000)    Positive precision: 0.957983 (0.0000)    Positive recall: 0.953975 (0.0000)    Negative precision: 0.975281 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.111003 (0.0000)    Log-loss reduction: 0.881154 (0.0000)    F1 Score: 0.955975 (0.0000)    AUPRC: 0.991883 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.01      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-TrainTest-breast-cancer-out.txt'  Saving summary with: SavePredictorAs in={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-model.zip} sum={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-summary.txt}  Saving predictor summary    Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-summary.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-summary.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-TrainTest-breast-cancer-summary.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 228 | 11 | 0.9540    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9580 | 0.9753 |    OVERALL 0/1 ACCURACY: 0.969253    LOG LOSS/instance: 0.111003    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.881154    AUC: 0.996136      OVERALL RESULTS    ---------------------------------------    AUC: 0.996136 (0.0000)    Accuracy: 0.969253 (0.0000)    Positive precision: 0.957983 (0.0000)    Positive recall: 0.953975 (0.0000)    Negative precision: 0.975281 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.111003 (0.0000)    Log-loss reduction: 0.881154 (0.0000)    F1 Score: 0.955975 (0.0000)    AUPRC: 0.991883 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.004      Suffix of length 34 compared against sequence of length 42  Running 'LogisticRegression' on 'breast-cancer'  Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-CV-breast-cancer.txt} norm=no threads-  maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-CV-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Not adding a normalizer.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Warning:  Skipped 8 instances with missing features/label/weight during training    Beginning optimization    num vars: 10    improvement criterion: Mean Improvement    L1 regularization selected 10 of 10 weights.    Not training a calibrator because it is not needed.    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 130 | 4 | 0.9701    negative || 7 | 213 | 0.9682    ||======================    Precision || 0.9489 | 0.9816 |    OVERALL 0/1 ACCURACY: 0.968927    LOG LOSS/instance: 0.143504    Test-set entropy (prior Log-Loss/instance): 0.956998    LOG-LOSS REDUCTION (RIG): 0.850048    AUC: 0.994132    Warning:  The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 96 | 9 | 0.9143    negative || 3 | 221 | 0.9866    ||======================    Precision || 0.9697 | 0.9609 |    OVERALL 0/1 ACCURACY: 0.963526    LOG LOSS/instance: 0.111794    Test-set entropy (prior Log-Loss/instance): 0.903454    LOG-LOSS REDUCTION (RIG): 0.876260    AUC: 0.997236      OVERALL RESULTS    ---------------------------------------    AUC: 0.995684 (0.0016)    Accuracy: 0.966226 (0.0027)    Positive precision: 0.959301 (0.0104)    Positive recall: 0.942217 (0.0279)    Negative precision: 0.971218 (0.0103)    Negative recall: 0.977394 (0.0092)    Log-loss: 0.127649 (0.0159)    Log-loss reduction: 0.863154 (0.0131)    F1 Score: 0.950293 (0.0091)    AUPRC: 0.991584 (0.0025)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.014      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-CV-breast-cancer-out.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-CV-breast-cancer-rp.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/LogisticRegression/LogisticRegression-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/LogisticRegression/LogisticRegression-CV-breast-cancer.txt  Output matches baseline: 'LogisticRegression/LogisticRegression-CV-breast-cancer.txt'  Test BinaryClassifierLogisticRegressionTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.MulticlassReductionTest with memory usage 158,334,976.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.MulticlassReductionTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesHeterogeneousTest  Running 'OVA' on 'iris'  Running as: TrainTest tr=OVA{p=AvgPer{ lr=0.8 }} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt xf=Term{col=Label} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-TrainTest-iris-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-TrainTest-iris.txt} norm=no  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=OVA{p=AvgPer{ lr=0.8 }} norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-TrainTest-iris-model.zip seed=1 xf=Term{col=Label}    Not adding a normalizer.    Training learner 0    Training calibrator.    Training learner 1    Training calibrator.    Training learner 2    Training calibrator.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 46 | 4 | 0.9200    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9583 |0.9231 |    Accuracy(micro-avg): 0.960000    Accuracy(macro-avg): 0.960000    Log-loss: 0.254771    Log-loss reduction: 0.768097      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.960000 (0.0000)    Accuracy(macro-avg): 0.960000 (0.0000)    Log-loss: 0.254771 (0.0000)    Log-loss reduction: 0.768097 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.023      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-TrainTest-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-TrainTest-iris-out.txt  Output matches baseline: 'OVA/OVA-TrainTest-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-TrainTest-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-TrainTest-iris-rp.txt  Output matches baseline: 'OVA/OVA-TrainTest-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-TrainTest-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-TrainTest-iris.txt  Output matches baseline: 'OVA/OVA-TrainTest-iris.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-TrainTest-iris-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 46 | 4 | 0.9200    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9583 |0.9231 |    Accuracy(micro-avg): 0.960000    Accuracy(macro-avg): 0.960000    Log-loss: 0.254771    Log-loss reduction: 0.768097      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.960000 (0.0000)    Accuracy(macro-avg): 0.960000 (0.0000)    Log-loss: 0.254771 (0.0000)    Log-loss reduction: 0.768097 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.008      Suffix of length 27 compared against sequence of length 36  Running 'OVA' on 'iris'  Running as: CV tr=OVA{p=AvgPer{ lr=0.8 }} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 xf=Term{col=Label} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-CV-iris.txt} norm=no threads-  maml.exe CV tr=OVA{p=AvgPer{ lr=0.8 }} threads=- norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-CV-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 xf=Term{col=Label}    Not adding a normalizer.    Training learner 0    Training calibrator.    Training learner 1    Training calibrator.    Training learner 2    Training calibrator.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Training learner 0    Training calibrator.    Training learner 1    Training calibrator.    Training learner 2    Training calibrator.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 21 | 0 | 0 | 1.0000    1 || 0 | 28 | 2 | 0.9333    2 || 0 | 0 | 28 | 1.0000    ||========================    Precision ||1.0000 |1.0000 |0.9333 |    Accuracy(micro-avg): 0.974684    Accuracy(macro-avg): 0.977778    Log-loss: 0.352944    Log-loss reduction: 0.675458      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 29 | 0 | 0 | 1.0000    1 || 0 | 18 | 2 | 0.9000    2 || 0 | 0 | 22 | 1.0000    ||========================    Precision ||1.0000 |1.0000 |0.9167 |    Accuracy(micro-avg): 0.971831    Accuracy(macro-avg): 0.966667    Log-loss: 0.273754    Log-loss reduction: 0.747843      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.973257 (0.0014)    Accuracy(macro-avg): 0.972222 (0.0056)    Log-loss: 0.313349 (0.0396)    Log-loss reduction: 0.711651 (0.0362)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.021      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-CV-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-CV-iris-out.txt  Output matches baseline: 'OVA/OVA-CV-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-CV-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-CV-iris-rp.txt  Output matches baseline: 'OVA/OVA-CV-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-CV-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-CV-iris.txt  Output matches baseline: 'OVA/OVA-CV-iris.txt'  Running 'OVA' on 'iris'  Running as: TrainTest tr=OVA{p=FastForest{ }} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt xf=Term{col=Label} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-TrainTest-iris-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-TrainTest-iris.txt} norm=no  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=OVA{p=FastForest{ }} norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-TrainTest-iris-model.zip seed=1 xf=Term{col=Label}    Not adding a normalizer.    Training learner 0    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 150 instances    Binning and forming Feature objects    Reserved memory for tree learner: 20436 bytes    Starting to train ...    Warning:  2 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.    Training calibrator.    Training learner 1    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 150 instances    Binning and forming Feature objects    Reserved memory for tree learner: 20436 bytes    Starting to train ...    Warning:  3 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.    Training calibrator.    Training learner 2    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 150 instances    Binning and forming Feature objects    Reserved memory for tree learner: 20436 bytes    Starting to train ...    Warning:  1 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.    Training calibrator.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 48 | 2 | 0.9600    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9600 |0.9600 |    Accuracy(micro-avg): 0.973333    Accuracy(macro-avg): 0.973333    Log-loss: 0.088201    Log-loss reduction: 0.919716      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.973333 (0.0000)    Accuracy(macro-avg): 0.973333 (0.0000)    Log-loss: 0.088201 (0.0000)    Log-loss reduction: 0.919716 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.075      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-TrainTest-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-FastForest-TrainTest-iris-out.txt  Output matches baseline: 'OVA/OVA-FastForest-TrainTest-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-TrainTest-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-FastForest-TrainTest-iris-rp.txt  Output matches baseline: 'OVA/OVA-FastForest-TrainTest-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-TrainTest-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-FastForest-TrainTest-iris.txt  Output matches baseline: 'OVA/OVA-FastForest-TrainTest-iris.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-TrainTest-iris-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 48 | 2 | 0.9600    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9600 |0.9600 |    Accuracy(micro-avg): 0.973333    Accuracy(macro-avg): 0.973333    Log-loss: 0.088201    Log-loss reduction: 0.919716      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.973333 (0.0000)    Accuracy(macro-avg): 0.973333 (0.0000)    Log-loss: 0.088201 (0.0000)    Log-loss reduction: 0.919716 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.012      Suffix of length 27 compared against sequence of length 57  Running 'OVA' on 'iris'  Running as: CV tr=OVA{p=FastForest{ }} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 xf=Term{col=Label} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-CV-iris.txt} norm=no threads-  maml.exe CV tr=OVA{p=FastForest{ }} threads=- norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-CV-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 xf=Term{col=Label}    Not adding a normalizer.    Training learner 0    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 71 instances    Binning and forming Feature objects    Reserved memory for tree learner: 16380 bytes    Starting to train ...    Warning:  2 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.    Training calibrator.    Training learner 1    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 71 instances    Binning and forming Feature objects    Reserved memory for tree learner: 16380 bytes    Starting to train ...    Warning:  3 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.    Training calibrator.    Training learner 2    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 71 instances    Binning and forming Feature objects    Reserved memory for tree learner: 16380 bytes    Starting to train ...    Warning:  1 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.    Training calibrator.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Training learner 0    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 79 instances    Binning and forming Feature objects    Reserved memory for tree learner: 17472 bytes    Starting to train ...    Warning:  2 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.    Training calibrator.    Training learner 1    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 79 instances    Binning and forming Feature objects    Reserved memory for tree learner: 17472 bytes    Starting to train ...    Warning:  3 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.    Training calibrator.    Training learner 2    Making per-feature arrays    Changing data from row-wise to column-wise    Processed 79 instances    Binning and forming Feature objects    Reserved memory for tree learner: 17472 bytes    Starting to train ...    Warning:  1 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.    Training calibrator.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 21 | 0 | 0 | 1.0000    1 || 0 | 25 | 5 | 0.8333    2 || 0 | 1 | 27 | 0.9643    ||========================    Precision ||1.0000 |0.9615 |0.8438 |    Accuracy(micro-avg): 0.924051    Accuracy(macro-avg): 0.932540    Log-loss: 0.197783    Log-loss reduction: 0.818133      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 29 | 0 | 0 | 1.0000    1 || 0 | 19 | 1 | 0.9500    2 || 0 | 2 | 20 | 0.9091    ||========================    Precision ||1.0000 |0.9048 |0.9524 |    Accuracy(micro-avg): 0.957746    Accuracy(macro-avg): 0.953030    Log-loss: 0.103360    Log-loss reduction: 0.904794      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.940899 (0.0168)    Accuracy(macro-avg): 0.942785 (0.0102)    Log-loss: 0.150571 (0.0472)    Log-loss reduction: 0.861464 (0.0433)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.106      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-CV-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-FastForest-CV-iris-out.txt  Output matches baseline: 'OVA/OVA-FastForest-CV-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-CV-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-FastForest-CV-iris-rp.txt  Output matches baseline: 'OVA/OVA-FastForest-CV-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/OVA/OVA-FastForest-CV-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/OVA/OVA-FastForest-CV-iris.txt  Output matches baseline: 'OVA/OVA-FastForest-CV-iris.txt'  Running 'PKPD' on 'iris'  Running as: TrainTest tr=PKPD{p=AvgPer { lr=0.8 }} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt xf=Term{col=Label} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-TrainTest-iris-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-TrainTest-iris.txt} norm=no  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=PKPD{p=AvgPer { lr=0.8 }} norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-TrainTest-iris-model.zip seed=1 xf=Term{col=Label}    Not adding a normalizer.    Training learner (0,0)    Training calibrator.    Training learner (1,0)    Training calibrator.    Training learner (1,1)    Training calibrator.    Training learner (2,0)    Training calibrator.    Training learner (2,1)    Training calibrator.    Training learner (2,2)    Training calibrator.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 46 | 4 | 0.9200    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9583 |0.9231 |    Accuracy(micro-avg): 0.960000    Accuracy(macro-avg): 0.960000    Log-loss: 0.255665    Log-loss reduction: 0.767284      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.960000 (0.0000)    Accuracy(macro-avg): 0.960000 (0.0000)    Log-loss: 0.255665 (0.0000)    Log-loss reduction: 0.767284 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.03      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-TrainTest-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PKPD/PKPD-TrainTest-iris-out.txt  Output matches baseline: 'PKPD/PKPD-TrainTest-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-TrainTest-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PKPD/PKPD-TrainTest-iris-rp.txt  Output matches baseline: 'PKPD/PKPD-TrainTest-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-TrainTest-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PKPD/PKPD-TrainTest-iris.txt  Output matches baseline: 'PKPD/PKPD-TrainTest-iris.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-TrainTest-iris-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 46 | 4 | 0.9200    2 || 0 | 2 | 48 | 0.9600    ||========================    Precision ||1.0000 |0.9583 |0.9231 |    Accuracy(micro-avg): 0.960000    Accuracy(macro-avg): 0.960000    Log-loss: 0.255665    Log-loss reduction: 0.767284      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.960000 (0.0000)    Accuracy(macro-avg): 0.960000 (0.0000)    Log-loss: 0.255665 (0.0000)    Log-loss reduction: 0.767284 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.009      Suffix of length 27 compared against sequence of length 42  Running 'PKPD' on 'iris'  Running as: CV tr=PKPD{p=AvgPer { lr=0.8 }} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 xf=Term{col=Label} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-CV-iris.txt} norm=no threads-  maml.exe CV tr=PKPD{p=AvgPer { lr=0.8 }} threads=- norm=No dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-CV-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 xf=Term{col=Label}    Not adding a normalizer.    Training learner (0,0)    Training calibrator.    Training learner (1,0)    Training calibrator.    Training learner (1,1)    Training calibrator.    Training learner (2,0)    Training calibrator.    Training learner (2,1)    Training calibrator.    Training learner (2,2)    Training calibrator.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Training learner (0,0)    Training calibrator.    Training learner (1,0)    Training calibrator.    Training learner (1,1)    Training calibrator.    Training learner (2,0)    Training calibrator.    Training learner (2,1)    Training calibrator.    Training learner (2,2)    Training calibrator.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 21 | 0 | 0 | 1.0000    1 || 0 | 28 | 2 | 0.9333    2 || 0 | 0 | 28 | 1.0000    ||========================    Precision ||1.0000 |1.0000 |0.9333 |    Accuracy(micro-avg): 0.974684    Accuracy(macro-avg): 0.977778    Log-loss: 0.359335    Log-loss reduction: 0.669582      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 29 | 0 | 0 | 1.0000    1 || 0 | 18 | 2 | 0.9000    2 || 0 | 0 | 22 | 1.0000    ||========================    Precision ||1.0000 |1.0000 |0.9167 |    Accuracy(micro-avg): 0.971831    Accuracy(macro-avg): 0.966667    Log-loss: 0.277823    Log-loss reduction: 0.744095      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.973257 (0.0014)    Accuracy(macro-avg): 0.972222 (0.0056)    Log-loss: 0.318579 (0.0408)    Log-loss reduction: 0.706839 (0.0373)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.026      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-CV-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PKPD/PKPD-CV-iris-out.txt  Output matches baseline: 'PKPD/PKPD-CV-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-CV-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PKPD/PKPD-CV-iris-rp.txt  Output matches baseline: 'PKPD/PKPD-CV-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/PKPD/PKPD-CV-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/PKPD/PKPD-CV-iris.txt  Output matches baseline: 'PKPD/PKPD-CV-iris.txt'  Test MulticlassReductionTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.WeightingClassificationNNPredictorsTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesHeterogeneousTest with memory usage 158,351,360.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesHeterogeneousTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomSubSpaceSelectorTest  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 20 learners for the batch 1    Beginning training model 1 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter)    Trainer 1 of 20 finished in 00:00:00.0008030    Beginning training model 2 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter)    Trainer 2 of 20 finished in 00:00:00.0017543    Beginning training model 3 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter)    Trainer 3 of 20 finished in 00:00:00.0002662    Beginning training model 4 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 120 instances with missing features during training (over 10 iterations; 12 inst/iter)    Trainer 4 of 20 finished in 00:00:00.0015665    Beginning training model 5 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 5 of 20 finished in 00:00:00.0002714    Beginning training model 6 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter)    Trainer 6 of 20 finished in 00:00:00.0047531    Beginning training model 7 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 7 of 20 finished in 00:00:00.0002710    Beginning training model 8 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter)    Trainer 8 of 20 finished in 00:00:00.0015037    Beginning training model 9 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 9 of 20 finished in 00:00:00.0002762    Beginning training model 10 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter)    Trainer 10 of 20 finished in 00:00:00.0015217    Beginning training model 11 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 11 of 20 finished in 00:00:00.0002554    Beginning training model 12 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 130 instances with missing features during training (over 10 iterations; 13 inst/iter)    Trainer 12 of 20 finished in 00:00:00.0015575    Beginning training model 13 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 13 of 20 finished in 00:00:00.0002591    Beginning training model 14 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 170 instances with missing features during training (over 10 iterations; 17 inst/iter)    Trainer 14 of 20 finished in 00:00:00.0014867    Beginning training model 15 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 15 of 20 finished in 00:00:00.0002672    Beginning training model 16 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 120 instances with missing features during training (over 10 iterations; 12 inst/iter)    Trainer 16 of 20 finished in 00:00:00.0025027    Beginning training model 17 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 17 of 20 finished in 00:00:00.0002612    Beginning training model 18 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 190 instances with missing features during training (over 10 iterations; 19 inst/iter)    Trainer 18 of 20 finished in 00:00:00.0014715    Beginning training model 19 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter)    Trainer 19 of 20 finished in 00:00:00.0002533    Beginning training model 20 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter)    Trainer 20 of 20 finished in 00:00:00.0015354    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9625 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.112863    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.879162    AUC: 0.996249      OVERALL RESULTS    ---------------------------------------    AUC: 0.996249 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.962500 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981941 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.112863 (0.0000)    Log-loss reduction: 0.879162 (0.0000)    F1 Score: 0.964509 (0.0000)    AUPRC: 0.992435 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.043      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/osx-arm64/WE-Hetero-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Hetero-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 9 | 435 | 0.9797    ||======================    Precision || 0.9625 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.975110    LOG LOSS/instance: 0.112863    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.879162    AUC: 0.996249      OVERALL RESULTS    ---------------------------------------    AUC: 0.996249 (0.0000)    Accuracy: 0.975110 (0.0000)    Positive precision: 0.962500 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981941 (0.0000)    Negative recall: 0.979730 (0.0000)    Log-loss: 0.112863 (0.0000)    Log-loss reduction: 0.879162 (0.0000)    F1 Score: 0.964509 (0.0000)    AUPRC: 0.992435 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:19 PM Time elapsed(s): 0.013      Suffix of length 34 compared against sequence of length 118  Test EnsemblesHeterogeneousTest: completed normally: passed  Test EnsemblesHeterogeneousTest is using osx-arm64 configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomSubSpaceSelectorTest with memory usage 158,466,048.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomSubSpaceSelectorTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombiner  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{nm=20 st=AllInstanceSelector{fs=RandomFeatureSelector} tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{nm=20 st=AllInstanceSelector{fs=RandomFeatureSelector} tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 20 learners for the batch 1    Beginning training model 1 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 1 of 20 finished in 00:00:00.0010940    Beginning training model 2 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 2 of 20 finished in 00:00:00.0004374    Beginning training model 3 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 3 of 20 finished in 00:00:00.0001875    Beginning training model 4 of 20    Trainer 4 of 20 finished in 00:00:00.0003823    Beginning training model 5 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 5 of 20 finished in 00:00:00.0003985    Beginning training model 6 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 6 of 20 finished in 00:00:00.0003595    Beginning training model 7 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 7 of 20 finished in 00:00:00.0003691    Beginning training model 8 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 8 of 20 finished in 00:00:00.0003656    Beginning training model 9 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 9 of 20 finished in 00:00:00.0003667    Beginning training model 10 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 10 of 20 finished in 00:00:00.0001936    Beginning training model 11 of 20    Trainer 11 of 20 finished in 00:00:00.0003476    Beginning training model 12 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 12 of 20 finished in 00:00:00.0001937    Beginning training model 13 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 13 of 20 finished in 00:00:00.0003137    Beginning training model 14 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 14 of 20 finished in 00:00:00.0003780    Beginning training model 15 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 15 of 20 finished in 00:00:00.0003209    Beginning training model 16 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 16 of 20 finished in 00:00:00.0003425    Beginning training model 17 of 20    Trainer 17 of 20 finished in 00:00:00.0003359    Beginning training model 18 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 18 of 20 finished in 00:00:00.0003250    Beginning training model 19 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 19 of 20 finished in 00:00:00.0003055    Beginning training model 20 of 20    Warning:  Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter)    Trainer 20 of 20 finished in 00:00:00.0002993    Training calibrator.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 233 | 8 | 0.9668    negative || 12 | 446 | 0.9738    ||======================    Precision || 0.9510 | 0.9824 |    OVERALL 0/1 ACCURACY: 0.971388    LOG LOSS/instance: 0.130701    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.859358    AUC: 0.994931      OVERALL RESULTS    ---------------------------------------    AUC: 0.994931 (0.0000)    Accuracy: 0.971388 (0.0000)    Positive precision: 0.951020 (0.0000)    Positive recall: 0.966805 (0.0000)    Negative precision: 0.982379 (0.0000)    Negative recall: 0.973799 (0.0000)    Log-loss: 0.130701 (0.0000)    Log-loss reduction: 0.859358 (0.0000)    F1 Score: 0.958848 (0.0000)    AUPRC: 0.989467 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.034      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-RandomFeature-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 233 | 8 | 0.9668    negative || 12 | 446 | 0.9738    ||======================    Precision || 0.9510 | 0.9824 |    OVERALL 0/1 ACCURACY: 0.971388    LOG LOSS/instance: 0.130701    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.859358    AUC: 0.994931      OVERALL RESULTS    ---------------------------------------    AUC: 0.994931 (0.0000)    Accuracy: 0.971388 (0.0000)    Positive precision: 0.951020 (0.0000)    Positive recall: 0.966805 (0.0000)    Negative precision: 0.982379 (0.0000)    Negative recall: 0.973799 (0.0000)    Log-loss: 0.130701 (0.0000)    Log-loss reduction: 0.859358 (0.0000)    F1 Score: 0.958848 (0.0000)    AUPRC: 0.989467 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.013      Suffix of length 33 compared against sequence of length 94  Test EnsemblesRandomSubSpaceSelectorTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombiner with memory usage 158,466,048.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombiner [PASS]  Output:  Test TestTreeEnsembleCombiner: aborted: passed  Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLinearSvmTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestPredictors.PoissonRegressorNonNegativeTest [SKIP]  Need CoreTLC specific baseline update Starting test: Microsoft.ML.RunTests.TestPredictors.MulticlassNaiveBayes Finished test: Microsoft.ML.RunTests.TestPredictors.MulticlassNaiveBayes with memory usage 158,498,816.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.MulticlassNaiveBayes [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesVotingCombinerTest  Running 'MultiClassNaiveBayes' on 'breast-cancer'  Running as: TrainTest tr=MultiClassNaiveBayes data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=MultiClassNaiveBayes cache=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Not training a calibrator because it is not needed.      Confusion table    ||======================    PREDICTED || 0 | 1 | Recall    TRUTH ||======================    0 || 458 | 0 | 1.0000    1 || 241 | 0 | 0.0000    ||======================    Precision || 0.6552 | 0.0000 |    Accuracy(micro-avg): 0.655222    Accuracy(macro-avg): 0.500000    Log-loss: 34.538776    Log-loss reduction: -52.618809      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.655222 (0.0000)    Accuracy(macro-avg): 0.500000 (0.0000)    Log-loss: 34.538776 (0.0000)    Log-loss reduction: -52.618809 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.012      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer-out.txt  Output matches baseline: 'MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer.txt  Output matches baseline: 'MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-TrainTest-breast-cancer-model.zip seed=1      Confusion table    ||======================    PREDICTED || 0 | 1 | Recall    TRUTH ||======================    0 || 458 | 0 | 1.0000    1 || 241 | 0 | 0.0000    ||======================    Precision || 0.6552 | 0.0000 |    Accuracy(micro-avg): 0.655222    Accuracy(macro-avg): 0.500000    Log-loss: 34.538776    Log-loss reduction: -52.618809      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.655222 (0.0000)    Accuracy(macro-avg): 0.500000 (0.0000)    Log-loss: 34.538776 (0.0000)    Log-loss reduction: -52.618809 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.007      Suffix of length 26 compared against sequence of length 29  Running 'MultiClassNaiveBayes' on 'breast-cancer'  Running as: CV tr=MultiClassNaiveBayes data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer.txt} threads-  maml.exe CV tr=MultiClassNaiveBayes threads=- cache=- dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1    Not adding a normalizer.    Not training a calibrator because it is not needed.    Not adding a normalizer.    Not training a calibrator because it is not needed.      Confusion table    ||======================    PREDICTED || 0 | 1 | Recall    TRUTH ||======================    0 || 228 | 0 | 1.0000    1 || 134 | 0 | 0.0000    ||======================    Precision || 0.6298 | 0.0000 |    Accuracy(micro-avg): 0.629834    Accuracy(macro-avg): 0.500000    Log-loss: 34.538776    Log-loss reduction: -51.407404      Confusion table    ||======================    PREDICTED || 0 | 1 | Recall    TRUTH ||======================    0 || 230 | 0 | 1.0000    1 || 107 | 0 | 0.0000    ||======================    Precision || 0.6825 | 0.0000 |    Accuracy(micro-avg): 0.682493    Accuracy(macro-avg): 0.500000    Log-loss: 34.538776    Log-loss reduction: -54.264136      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.656163 (0.0263)    Accuracy(macro-avg): 0.500000 (0.0000)    Log-loss: 34.538776 (0.0000)    Log-loss reduction: -52.835770 (1.4284)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.009      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer-out.txt  Output matches baseline: 'MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer-rp.txt  Output matches baseline: 'MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer.txt  Output matches baseline: 'MultiClassNaiveBayes/MultiClassNaiveBayes-CV-breast-cancer.txt'  Test MulticlassNaiveBayes: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.WeightingFastForestClassificationPredictorsTest [SKIP]  Need CoreTLC specific baseline update  Microsoft.ML.RunTests.TestPredictors.WeightingFastForestRegressionPredictorsTest [SKIP]  Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesVotingCombinerTest with memory usage 158,498,816.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesVotingCombinerTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiStackCombinerTest  Running 'WeightedEnsemble' on 'breast-cancer'  Running as: TrainTest tr=WeightedEnsemble{nm=20 oc=Voting tp=-} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=WeightedEnsemble{nm=20 oc=Voting tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-model.zip seed=1    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 20 learners for the batch 1    Beginning training model 1 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter)    Trainer 1 of 20 finished in 00:00:00.0005735    Beginning training model 2 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 2 of 20 finished in 00:00:00.0004994    Beginning training model 3 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter)    Trainer 3 of 20 finished in 00:00:00.0002655    Beginning training model 4 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 4 of 20 finished in 00:00:00.0002724    Beginning training model 5 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 5 of 20 finished in 00:00:00.0002664    Beginning training model 6 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 6 of 20 finished in 00:00:00.0002677    Beginning training model 7 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 7 of 20 finished in 00:00:00.0002657    Beginning training model 8 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 8 of 20 finished in 00:00:00.0002577    Beginning training model 9 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter)    Trainer 9 of 20 finished in 00:00:00.0002747    Beginning training model 10 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 10 of 20 finished in 00:00:00.0002641    Beginning training model 11 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 11 of 20 finished in 00:00:00.0002547    Beginning training model 12 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter)    Trainer 12 of 20 finished in 00:00:00.0002683    Beginning training model 13 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter)    Trainer 13 of 20 finished in 00:00:00.0002561    Beginning training model 14 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter)    Trainer 14 of 20 finished in 00:00:00.0002622    Beginning training model 15 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter)    Trainer 15 of 20 finished in 00:00:00.0002658    Beginning training model 16 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 16 of 20 finished in 00:00:00.0002616    Beginning training model 17 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter)    Trainer 17 of 20 finished in 00:00:00.0002556    Beginning training model 18 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter)    Trainer 18 of 20 finished in 00:00:00.0002546    Beginning training model 19 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter)    Trainer 19 of 20 finished in 00:00:00.0002500    Beginning training model 20 of 20    Warning:  Training data does not support shuffling, so ignoring request to shuffle    Warning:  Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter)    Trainer 20 of 20 finished in 00:00:00.0002634    Training calibrator.    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9585 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.129466    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.861385    AUC: 0.992339      OVERALL RESULTS    ---------------------------------------    AUC: 0.992339 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.958506 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981900 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.129466 (0.0000)    Log-loss reduction: 0.861385 (0.0000)    F1 Score: 0.962500 (0.0000)    AUPRC: 0.963675 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.029      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-out.txt  Output matches baseline: 'WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer.txt  Output matches baseline: 'WeightedEnsemble/WE-Voting-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsemble/WE-Voting-TrainTest-breast-cancer-model.zip seed=1    Warning:  The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.    TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 231 | 8 | 0.9665    negative || 10 | 434 | 0.9775    ||======================    Precision || 0.9585 | 0.9819 |    OVERALL 0/1 ACCURACY: 0.973646    LOG LOSS/instance: 0.129466    Test-set entropy (prior Log-Loss/instance): 0.934003    LOG-LOSS REDUCTION (RIG): 0.861385    AUC: 0.992339      OVERALL RESULTS    ---------------------------------------    AUC: 0.992339 (0.0000)    Accuracy: 0.973646 (0.0000)    Positive precision: 0.958506 (0.0000)    Positive recall: 0.966527 (0.0000)    Negative precision: 0.981900 (0.0000)    Negative recall: 0.977477 (0.0000)    Log-loss: 0.129466 (0.0000)    Log-loss reduction: 0.861385 (0.0000)    F1 Score: 0.962500 (0.0000)    AUPRC: 0.963675 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.012      Suffix of length 34 compared against sequence of length 118  Test EnsemblesVotingCombinerTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiStackCombinerTest with memory usage 158,531,584.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiStackCombinerTest [PASS]  Output: Starting test: Microsoft.ML.RunTests.TestPredictors.GamBinaryClassificationTest  Running 'WeightedEnsembleMulticlass' on 'iris'  Running as: TrainTest tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-} data=/tmp/helix/working/99790869/p/test/data/iris.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/iris.txt xf=Term{col=Label} out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/iris.txt tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-model.zip seed=1 xf=Term{col=Label}    Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.    Training 5 learners for the batch 1    Beginning training model 1 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 10 of 15 weights.    Trainer 1 of 5 finished in 00:00:00.0145977    Beginning training model 2 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 12 of 15 weights.    Trainer 2 of 5 finished in 00:00:00.0094697    Beginning training model 3 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 3 of 5 finished in 00:00:00.0077770    Beginning training model 4 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 11 of 15 weights.    Trainer 4 of 5 finished in 00:00:00.0099957    Beginning training model 5 of 5    Beginning optimization    num vars: 15    improvement criterion: Mean Improvement    L1 regularization selected 13 of 15 weights.    Trainer 5 of 5 finished in 00:00:00.0100121    The number of instances used for stacking trainer is 43    Warning:  The trainer specified for stacking wants normalization, but we do not currently allow this.    Beginning optimization    num vars: 48    improvement criterion: Mean Improvement    L1 regularization selected 26 of 48 weights.    Not training a calibrator because it is not needed.      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 35 | 15 | 0.7000    2 || 0 | 0 | 50 | 1.0000    ||========================    Precision ||1.0000 |1.0000 |0.7692 |    Accuracy(micro-avg): 0.900000    Accuracy(macro-avg): 0.900000    Log-loss: 0.430565    Log-loss reduction: 0.608083      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.900000 (0.0000)    Accuracy(macro-avg): 0.900000 (0.0000)    Log-loss: 0.430565 (0.0000)    Log-loss reduction: 0.608083 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.068      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Stacking-TrainTest-iris-out.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp/WE-Stacking-TrainTest-iris-rp.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/WeightedEnsembleMulticlass/osx-arm64/WE-Stacking-TrainTest-iris.txt  Output matches baseline: 'WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris.txt data=/tmp/helix/working/99790869/p/test/data/iris.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/WeightedEnsembleMulticlass/WE-Stacking-TrainTest-iris-model.zip seed=1      Confusion table    ||========================    PREDICTED || 0 | 1 | 2 | Recall    TRUTH ||========================    0 || 50 | 0 | 0 | 1.0000    1 || 0 | 35 | 15 | 0.7000    2 || 0 | 0 | 50 | 1.0000    ||========================    Precision ||1.0000 |1.0000 |0.7692 |    Accuracy(micro-avg): 0.900000    Accuracy(macro-avg): 0.900000    Log-loss: 0.430565    Log-loss reduction: 0.608083      OVERALL RESULTS    ---------------------------------------    Accuracy(micro-avg): 0.900000 (0.0000)    Accuracy(macro-avg): 0.900000 (0.0000)    Log-loss: 0.430565 (0.0000)    Log-loss reduction: 0.608083 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.008      Suffix of length 27 compared against sequence of length 67  Test EnsemblesMultiStackCombinerTest: completed normally: passed  Test EnsemblesMultiStackCombinerTest is using osx-arm64 configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.GamBinaryClassificationTest with memory usage 157,941,760.00 and max memory usage 0.00  Microsoft.ML.RunTests.TestPredictors.GamBinaryClassificationTest [PASS]  Output:  Running 'BinaryClassificationGamTrainer' on 'breast-cancer'  Running as: TrainTest tr=BinaryClassificationGamTrainer data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=BinaryClassificationGamTrainer dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise    Warning:  Skipped 16 instances with missing features during training    Processed 683 instances    Binning and forming Feature objects    Starting to train ...    Training calibrator.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 9 | 0.9627    negative || 12 | 446 | 0.9738    ||======================    Precision || 0.9508 | 0.9802 |    OVERALL 0/1 ACCURACY: 0.969957    LOG LOSS/instance: 0.113509    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.877858    AUC: 0.994972      OVERALL RESULTS    ---------------------------------------    AUC: 0.994972 (0.0000)    Accuracy: 0.969957 (0.0000)    Positive precision: 0.950820 (0.0000)    Positive recall: 0.962656 (0.0000)    Negative precision: 0.980220 (0.0000)    Negative recall: 0.973799 (0.0000)    Log-loss: 0.113509 (0.0000)    Log-loss reduction: 0.877858 (0.0000)    F1 Score: 0.956701 (0.0000)    AUPRC: 0.989577 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.437      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt  Output matches baseline: 'BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt  Output matches baseline: 'BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 9 | 0.9627    negative || 12 | 446 | 0.9738    ||======================    Precision || 0.9508 | 0.9802 |    OVERALL 0/1 ACCURACY: 0.969957    LOG LOSS/instance: 0.113509    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.877858    AUC: 0.994972      OVERALL RESULTS    ---------------------------------------    AUC: 0.994972 (0.0000)    Accuracy: 0.969957 (0.0000)    Positive precision: 0.950820 (0.0000)    Positive recall: 0.962656 (0.0000)    Negative precision: 0.980220 (0.0000)    Negative recall: 0.973799 (0.0000)    Log-loss: 0.113509 (0.0000)    Log-loss reduction: 0.877858 (0.0000)    F1 Score: 0.956701 (0.0000)    AUPRC: 0.989577 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:20 PM Time elapsed(s): 0.006      Suffix of length 33 compared against sequence of length 42  Running 'BinaryClassificationGamTrainer' on 'breast-cancer'  Running as: TrainTest tr=BinaryClassificationGamTrainer{dt+} data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt seed=1 test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-model.zip} dout={/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt}  maml.exe TrainTest test=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt tr=BinaryClassificationGamTrainer{dt+} dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt out=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-model.zip seed=1    Not adding a normalizer.    Making per-feature arrays    Changing data from row-wise to column-wise on disk    Warning:  16 of 699 examples will be skipped due to missing feature values    Processed 683 instances    Binning and forming Feature objects    Starting to train ...    Training calibrator.    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 9 | 0.9627    negative || 12 | 446 | 0.9738    ||======================    Precision || 0.9508 | 0.9802 |    OVERALL 0/1 ACCURACY: 0.969957    LOG LOSS/instance: 0.113509    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.877858    AUC: 0.994972      OVERALL RESULTS    ---------------------------------------    AUC: 0.994972 (0.0000)    Accuracy: 0.969957 (0.0000)    Positive precision: 0.950820 (0.0000)    Positive recall: 0.962656 (0.0000)    Negative precision: 0.980220 (0.0000)    Negative recall: 0.973799 (0.0000)    Log-loss: 0.113509 (0.0000)    Log-loss reduction: 0.877858 (0.0000)    F1 Score: 0.956701 (0.0000)    AUPRC: 0.989577 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:21 PM Time elapsed(s): 0.449      Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt  Output matches baseline: 'BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt  Output matches baseline: 'BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt'  Comparing /private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt and /tmp/helix/working/99790869/p/test/BaselineOutput/Common/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt  Output matches baseline: 'BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt'  maml.exe Test dout=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt data=/tmp/helix/working/99790869/p/test/data/breast-cancer.txt in=/private/tmp/helix/working/99790869/w/AD3309DB/e/TestOutput/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-model.zip seed=1    TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))    Confusion table    ||======================    PREDICTED || positive | negative | Recall    TRUTH ||======================    positive || 232 | 9 | 0.9627    negative || 12 | 446 | 0.9738    ||======================    Precision || 0.9508 | 0.9802 |    OVERALL 0/1 ACCURACY: 0.969957    LOG LOSS/instance: 0.113509    Test-set entropy (prior Log-Loss/instance): 0.929318    LOG-LOSS REDUCTION (RIG): 0.877858    AUC: 0.994972      OVERALL RESULTS    ---------------------------------------    AUC: 0.994972 (0.0000)    Accuracy: 0.969957 (0.0000)    Positive precision: 0.950820 (0.0000)    Positive recall: 0.962656 (0.0000)    Negative precision: 0.980220 (0.0000)    Negative recall: 0.973799 (0.0000)    Log-loss: 0.113509 (0.0000)    Log-loss reduction: 0.877858 (0.0000)    F1 Score: 0.956701 (0.0000)    AUPRC: 0.989577 (0.0000)      ---------------------------------------    Physical memory usage(MB): 0    Virtual memory usage(MB): 0    09/21/2026 13:24:21 PM Time elapsed(s): 0.004      Suffix of length 33 compared against sequence of length 42  Test GamBinaryClassificationTest: completed normally: passed  Microsoft.ML.RunTests.TestPredictors.RegressorOlsTestOne [SKIP]  This test requires a native library MklImports that wasn't found.  Microsoft.ML.RunTests.TestPredictors.FastTreeRankingTest [SKIP]  Need CoreTLC specific baseline update  Finished: Microsoft.ML.Predictor.Tests === TEST EXECUTION SUMMARY ===  Microsoft.ML.Predictor.Tests Total: 115, Errors: 0, Failed: 1, Skipped: 56, Time: 14.145s /private/tmp/helix/working/99790869/w/AD3309DB/e ----- end Mon Sep 21 06:24:21 PDT 2026 ----- exit code 1 ---------------------------------------------------------- + export _commandExitCode=0 + _commandExitCode=0 + /opt/homebrew/Cellar/python@3.14/3.14.6/Frameworks/Python.framework/Versions/3.14/bin/python3.14 /tmp/helix/working/99790869/p/reporter/run.py https://dev.azure.com/dnceng-public/ public 44346754 eyJ0eXAiOiJKV1QiLCJhbGciOiJSUzI1NiIsIng1dCI6ImRndlNEdks4QTVLeUt5cHB3MWRBd1RYRDNDQSIsImtpZCI6ImRndlNEdks4QTVLeUt5cHB3MWRBd1RYRDNDQSJ9.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.OqwLK0-Fsv61sL_k6izxBbDMxZRfXFYzhVn3UPnMW20MZnEFoimYNJPXyolHN6XjbVXKh2dlssFYDUjV78RD2nbKVVP8Dbb8-S2zlVbZ4wMZoUFsOGJ4E8F1ZDJAphm8HM8D0vLzkhx29liXB4rmXTfKttysJiPhwWiaLGB2XiUP5tprKrco-w0eT4xmaVRjG-IGxcoBNAYh3Z5uRP0QvjaSgXzzEY7o1G4OfLlAWZ0P4Kl-tRq_Ee1elYy6eLfIPisEOBg9uuiZcc-2qvh-hiRiLtZvKxB3U1nCil-nTDFbsr6vDUCaXuED8wsL7v5aokFk23E_PbheFzW843_3aA /etc/helix/scripts/azure/__init__.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources 2026-09-21T13:24:21.849Z INFO run.py azure_utils(46) get_credential_and_access_token Using /etc/helix-prep/client.pem certificate for e119e735-021e-4931-8059-7221fe1e3d1d 2026-09-21T13:24:21.940Z INFO run.py _universal(535) on_request Request URL: 'https://login.microsoftonline.com/72f988bf-86f1-41af-91ab-2d7cd011db47/v2.0/.well-known/openid-configuration' Request method: 'GET' Request headers: 'User-Agent': 'azsdk-python-identity/1.25.3 Python/3.14.6 (macOS-15.7-arm64-arm-64bit-Mach-O)' No body was attached to the request 2026-09-21T13:24:22.115Z INFO run.py _universal(581) on_response Response status: 200 Response headers: 'Cache-Control': 'max-age=86400, private' 'Content-Type': 'application/json; charset=utf-8' 'Strict-Transport-Security': 'REDACTED' 'X-Content-Type-Options': 'REDACTED' 'Access-Control-Allow-Origin': 'REDACTED' 'Access-Control-Allow-Methods': 'REDACTED' 'P3P': 'REDACTED' 'x-ms-request-id': '42fcb8b6-c475-4513-97d2-d58f6d580500' 'x-ms-ests-server': 'REDACTED' 'x-ms-srs': 'REDACTED' 'Content-Security-Policy': 'REDACTED' 'X-XSS-Protection': 'REDACTED' 'Set-Cookie': 'REDACTED' 'Date': 'Mon, 21 Sep 2026 13:24:22 GMT' 'Content-Length': '1964' 2026-09-21T13:24:22.242Z INFO run.py _universal(532) on_request Request URL: 'https://login.microsoftonline.com/72f988bf-86f1-41af-91ab-2d7cd011db47/oauth2/v2.0/token' Request method: 'POST' Request headers: 'Content-Type': 'application/x-www-form-urlencoded' 'Accept': 'application/json' 'x-client-sku': 'REDACTED' 'x-client-ver': 'REDACTED' 'x-client-os': 'REDACTED' 'x-ms-lib-capability': 'REDACTED' 'client-request-id': 'REDACTED' 'x-client-current-telemetry': 'REDACTED' 'x-client-last-telemetry': 'REDACTED' 'User-Agent': 'azsdk-python-identity/1.25.3 Python/3.14.6 (macOS-15.7-arm64-arm-64bit-Mach-O)' A body is sent with the request 2026-09-21T13:24:22.297Z INFO run.py _universal(581) on_response Response status: 200 Response headers: 'Cache-Control': 'no-store, no-cache' 'Pragma': 'no-cache' 'Content-Type': 'application/json; charset=utf-8' 'Expires': '-1' 'Strict-Transport-Security': 'REDACTED' 'X-Content-Type-Options': 'REDACTED' 'x-ms-clientdata': 'REDACTED' 'P3P': 'REDACTED' 'client-request-id': 'REDACTED' 'x-ms-request-id': '3d5f3cde-26de-48cb-a0bc-e5262e260700' 'x-ms-ests-server': 'REDACTED' 'x-ms-clitelem': 'REDACTED' 'x-ms-srs': 'REDACTED' 'Content-Security-Policy': 'REDACTED' 'X-XSS-Protection': 'REDACTED' 'Set-Cookie': 'REDACTED' 'Date': 'Mon, 21 Sep 2026 13:24:22 GMT' 'Content-Length': '2005' 2026-09-21T13:24:22.297Z INFO run.py get_token_mixin(139) _get_token_base CertificateCredential.get_token succeeded 2026-09-21T13:24:22.297Z INFO run.py azure_utils(64) get_credential_and_access_token Credentials are valid 2026-09-21T13:24:22.298Z INFO run.py get_token_mixin(139) _get_token_base CertificateCredential.get_token_info succeeded 2026-09-21T13:24:22.383Z INFO run.py run(67) main Beginning reading of test results. 2026-09-21T13:24:22.384Z INFO run.py __init__(49) read_results Searching '/private/tmp/helix/working/99790869/w/AD3309DB/e' for test results files 2026-09-21T13:24:22.384Z INFO run.py __init__(55) read_results Found results file /private/tmp/helix/working/99790869/w/AD3309DB/e/testResults.xml with format xunit 2026-09-21T13:24:22.394Z INFO run.py __init__(49) read_results Searching '/tmp/helix/working/99790869/w/AD3309DB/uploads' for test results files 2026-09-21T13:24:22.395Z INFO run.py packing_test_reporter(30) report_results Packing 115 test reports to '/tmp/helix/working/99790869/w/AD3309DB/e/__test_report.json' 2026-09-21T13:24:22.395Z INFO run.py packing_test_reporter(33) report_results Packed 55007 bytes 2026-09-21T13:24:22.395Z INFO run.py _helix_compat(255) report_results Writing 0 test results to '/tmp/helix/working/99790869/w/AD3309DB/e/__test_report_v2.json' (JSON v1) 2026-09-21T13:24:22.395Z INFO run.py _helix_compat(261) report_results Wrote 2619 bytes to '/tmp/helix/working/99790869/w/AD3309DB/e/__test_report_v2.json' + exit 0 ['Microsoft.ML.Predictor.Tests' END OF WORK ITEM LOG: Command exited with 0]