From adad3e4c3104539932b0ab9f3aa110612c5e844d Mon Sep 17 00:00:00 2001
From: frank-dong-ms <55860649+frank-dong-ms@users.noreply.github.com>
Date: Thu, 23 Apr 2020 10:00:36 -0700
Subject: [PATCH] NetCore3.1 generates different test result issue (#5047)
* fix difference by using different result
* add netcore3.1 baseline results
* sync latest and merge
* modify configuration specific baselines to support combination of configuration
* take comments
* remove unnecessary changes
* take comments
---
.../CommandTrainMlrWithStats-out.txt | 15 +
.../CommandTrainMlrWithStats-summary.txt | 17 +
.../LDSVM-def-CV-breast-cancer-out.txt | 56 ++
.../LDSVM-def-CV-breast-cancer-rp.txt | 4 +
.../LDSVM-def-CV-breast-cancer.txt | 700 ++++++++++++++++++
.../LDSVM-def-TrainTest-breast-cancer-out.txt | 38 +
.../LDSVM-def-TrainTest-breast-cancer-rp.txt | 4 +
.../LDSVM-def-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++
.../LDSVM-nob-CV-breast-cancer-out.txt | 56 ++
.../LDSVM-nob-CV-breast-cancer-rp.txt | 4 +
.../LDSVM-nob-CV-breast-cancer.txt | 700 ++++++++++++++++++
.../LDSVM-nob-TrainTest-breast-cancer-out.txt | 38 +
.../LDSVM-nob-TrainTest-breast-cancer-rp.txt | 4 +
.../LDSVM-nob-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++
.../WE-Average-TrainTest-iris-out.txt | 61 ++
.../WE-Average-TrainTest-iris-rp.txt | 4 +
.../WE-Average-TrainTest-iris.txt | 151 ++++
.../WE-Bootstrap-TrainTest-iris-out.txt | 151 ++++
.../WE-Bootstrap-TrainTest-iris-rp.txt | 4 +
.../WE-Bootstrap-TrainTest-iris.txt | 151 ++++
.../WE-Stacking-TrainTest-iris-out.txt | 67 ++
.../WE-Stacking-TrainTest-iris-rp.txt | 4 +
.../WE-Stacking-TrainTest-iris.txt | 151 ++++
.../WE-Voting-TrainTest-iris-out.txt | 61 ++
...on-CV-breast-cancer.PAVcalibration-out.txt | 58 ++
...ron-CV-breast-cancer.PAVcalibration-rp.txt | 4 +
...eptron-CV-breast-cancer.PAVcalibration.txt | 700 ++++++++++++++++++
...n-CV-breast-cancer.calibrateRandom-out.txt | 56 ++
...on-CV-breast-cancer.calibrateRandom-rp.txt | 4 +
...ptron-CV-breast-cancer.calibrateRandom.txt | 700 ++++++++++++++++++
...ron-CV-breast-cancer.nocalibration-out.txt | 58 ++
...tron-CV-breast-cancer.nocalibration-rp.txt | 4 +
...ceptron-CV-breast-cancer.nocalibration.txt | 700 ++++++++++++++++++
...TrainTest-breast-cancer.PAVcalibration.txt | 700 ++++++++++++++++++
...rainTest-breast-cancer.calibrateRandom.txt | 700 ++++++++++++++++++
...-TrainTest-breast-cancer.nocalibration.txt | 700 ++++++++++++++++++
...sion-non-negative-CV-breast-cancer-out.txt | 121 +++
...ssion-non-negative-CV-breast-cancer-rp.txt | 4 +
...gression-non-negative-CV-breast-cancer.txt | 700 ++++++++++++++++++
...n-negative-TrainTest-breast-cancer-out.txt | 71 ++
...n-non-negative-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++
...lticlassLogisticRegression-CV-iris-out.txt | 111 +++
.../MulticlassLogisticRegression-CV-iris.txt | 151 ++++
...classLogisticRegression-TrainTest-iris.txt | 151 ++++
...on-CV-breast-cancer.PAVcalibration-out.txt | 58 ++
...ron-CV-breast-cancer.PAVcalibration-rp.txt | 4 +
...eptron-CV-breast-cancer.PAVcalibration.txt | 700 ++++++++++++++++++
...n-CV-breast-cancer.calibrateRandom-out.txt | 56 ++
...on-CV-breast-cancer.calibrateRandom-rp.txt | 4 +
...ptron-CV-breast-cancer.calibrateRandom.txt | 700 ++++++++++++++++++
...ron-CV-breast-cancer.nocalibration-out.txt | 58 ++
...tron-CV-breast-cancer.nocalibration-rp.txt | 4 +
...ceptron-CV-breast-cancer.nocalibration.txt | 700 ++++++++++++++++++
...TrainTest-breast-cancer.PAVcalibration.txt | 700 ++++++++++++++++++
...rainTest-breast-cancer.calibrateRandom.txt | 700 ++++++++++++++++++
...-TrainTest-breast-cancer.nocalibration.txt | 700 ++++++++++++++++++
...sion-non-negative-CV-breast-cancer-out.txt | 121 +++
...ssion-non-negative-CV-breast-cancer-rp.txt | 4 +
...gression-non-negative-CV-breast-cancer.txt | 700 ++++++++++++++++++
...n-negative-TrainTest-breast-cancer-out.txt | 71 ++
...n-non-negative-TrainTest-breast-cancer.txt | 700 ++++++++++++++++++
...lticlassLogisticRegression-CV-iris-out.txt | 111 +++
.../MulticlassLogisticRegression-CV-iris.txt | 151 ++++
...classLogisticRegression-TrainTest-iris.txt | 151 ++++
.../TestPredictors.cs | 24 +-
...etCore30OrNotNetCoreAndX64FactAttribute.cs | 41 -
...sThanNetCore30OrNotNetCoreFactAttribute.cs | 25 -
.../BaseTestBaseline.cs | 63 +-
.../TestCommandBase.cs | 2 +-
.../TrainerEstimators/LbfgsTests.cs | 7 +-
70 files changed, 16692 insertions(+), 97 deletions(-)
create mode 100644 test/BaselineOutput/Common/Command/netcoreapp31/CommandTrainMlrWithStats-out.txt
create mode 100644 test/BaselineOutput/Common/Command/netcoreapp31/CommandTrainMlrWithStats-summary.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer-out.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer-rp.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer-out.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer-rp.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer-out.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer-rp.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer-out.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer-rp.txt
create mode 100644 test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer.txt
create mode 100644 test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris-out.txt
create mode 100644 test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris-rp.txt
create mode 100644 test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris.txt
create mode 100644 test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris-out.txt
create mode 100644 test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris-rp.txt
create mode 100644 test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris.txt
create mode 100644 test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris-out.txt
create mode 100644 test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris-rp.txt
create mode 100644 test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris.txt
create mode 100644 test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Voting-TrainTest-iris-out.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt
create mode 100644 test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt
create mode 100644 test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-out.txt
create mode 100644 test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-rp.txt
create mode 100644 test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer.txt
create mode 100644 test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt
create mode 100644 test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer.txt
create mode 100644 test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris-out.txt
create mode 100644 test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris.txt
create mode 100644 test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-TrainTest-iris.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt
create mode 100644 test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt
create mode 100644 test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-out.txt
create mode 100644 test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-rp.txt
create mode 100644 test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer.txt
create mode 100644 test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt
create mode 100644 test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer.txt
create mode 100644 test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris-out.txt
create mode 100644 test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris.txt
create mode 100644 test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-TrainTest-iris.txt
delete mode 100644 test/Microsoft.ML.TestFramework/Attributes/LessThanNetCore30OrNotNetCoreAndX64FactAttribute.cs
delete mode 100644 test/Microsoft.ML.TestFramework/Attributes/LessThanNetCore30OrNotNetCoreFactAttribute.cs
diff --git a/test/BaselineOutput/Common/Command/netcoreapp31/CommandTrainMlrWithStats-out.txt b/test/BaselineOutput/Common/Command/netcoreapp31/CommandTrainMlrWithStats-out.txt
new file mode 100644
index 0000000000..bc48e58c54
--- /dev/null
+++ b/test/BaselineOutput/Common/Command/netcoreapp31/CommandTrainMlrWithStats-out.txt
@@ -0,0 +1,15 @@
+maml.exe Train tr=MultiClassLogisticRegression{maxiter=100 t=- stat=+} loader=TextLoader{col=Label:TX:4 col=Features:R4:0-3 sep=,} data=%Data% out=%Output% seed=1 xf=Term{col=Label}
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 10 of 15 weights.
+Model trained with 150 training examples.
+Residual Deviance: 132.4371
+Null Deviance: 329.58368
+AIC: 152.4371
+Not training a calibrator because it is not needed.
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/Common/Command/netcoreapp31/CommandTrainMlrWithStats-summary.txt b/test/BaselineOutput/Common/Command/netcoreapp31/CommandTrainMlrWithStats-summary.txt
new file mode 100644
index 0000000000..1204009799
--- /dev/null
+++ b/test/BaselineOutput/Common/Command/netcoreapp31/CommandTrainMlrWithStats-summary.txt
@@ -0,0 +1,17 @@
+LbfgsMaximumEntropyMulticlassTrainer bias and non-zero weights
+ Iris-setosa+(Bias) 2.2171915
+ Iris-versicolor+(Bias) 0.76931
+ Iris-virginica+(Bias) -2.9864972
+ Iris-setosa+f3 -3.179184
+ Iris-setosa+f2 -2.8718326
+ Iris-setosa+f1 0.5830593
+ Iris-versicolor+f1 -0.68959576
+ Iris-virginica+f3 3.145027
+ Iris-virginica+f2 1.882819
+ Iris-virginica+f0 0.0037482954
+
+*** MODEL STATISTICS SUMMARY ***
+Count of training examples: 150
+Residual Deviance: 132.4371
+Null Deviance: 329.58368
+AIC: 152.4371
diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer-out.txt
new file mode 100644
index 0000000000..e9a8e91fcc
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer-out.txt
@@ -0,0 +1,56 @@
+maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% 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 || 12 | 208 | 0.9455
+ ||======================
+Precision || 0.9178 | 1.0000 |
+OVERALL 0/1 ACCURACY: 0.966102
+LOG LOSS/instance: 0.121887
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): 0.872636
+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 || 101 | 4 | 0.9619
+ negative || 5 | 219 | 0.9777
+ ||======================
+Precision || 0.9528 | 0.9821 |
+OVERALL 0/1 ACCURACY: 0.972644
+LOG LOSS/instance: 0.160647
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.822185
+AUC: 0.984694
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.989565 (0.0049)
+Accuracy: 0.969373 (0.0033)
+Positive precision: 0.935319 (0.0175)
+Positive recall: 0.980952 (0.0190)
+Negative precision: 0.991031 (0.0090)
+Negative recall: 0.961567 (0.0161)
+Log-loss: 0.141267 (0.0194)
+Log-loss reduction: 0.847411 (0.0252)
+F1 Score: 0.957244 (0.0001)
+AUPRC: 0.986457 (0.0034)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer-rp.txt
new file mode 100644
index 0000000000..cc548ed2c1
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+LdSvm
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.989565 0.969373 0.935319 0.980952 0.991031 0.961567 0.141267 0.847411 0.957244 0.986457 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1 /iter:1000
+
diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer.txt
new file mode 100644
index 0000000000..c3cf88efc4
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
+5 1 3.0474453 0.9994966 0.00072646560889982241 1
+6 0 0.20119351 0.3968446 0.72939833128640874 1
+8 0 -1.2698665 0.0103569245 0.015019798026328253 0
+9 0 -1.3894278 0.0074190605 0.010743343940404436 0
+10 0 -1.344451 0.008412023 0.012187316136918923 0
+11 0 -1.3877528 0.0074538635 0.01079393032469096 0
+18 1 1.8441013 0.9853157 0.021342078203631754 1
+20 1 1.8220574 0.98439026 0.02269771346902881 1
+21 1 2.0383902 0.99144894 0.012389621545417531 1
+25 1 0.686989 0.72089237 0.47214421575833482 1
+28 0 -1.3877528 0.0074538635 0.01079393032469096 0
+31 0 -1.3567832 0.00812734 0.011773180672032832 0
+32 1 2.1794708 0.9942355 0.0083404554402042134 1
+35 0 -1.3877528 0.0074538635 0.01079393032469096 0
+37 0 -1.1515085 0.014393092 0.020915727338326071 0
+40 0 ? ? ? 0
+41 1 1.1106282 0.8948665 0.16025558173388371 1
+44 1 2.4147348 0.99701905 0.0043070208631295851 1
+45 0 -1.3475968 0.008338478 0.012080317449367115 0
+46 1 1.3397874 0.94194365 0.08628734590117855 1
+48 0 -1.3355757 0.008623002 0.012494309345479765 0
+50 1 0.93614846 0.83892894 0.25337948358329276 1
+51 1 0.44402552 0.56585175 0.82150397549816123 1
+52 1 1.3920381 0.94948345 0.074785234809580145 1
+54 1 2.0682673 0.99213326 0.011394183648129512 1
+56 1 1.4756995 0.95965576 0.059411105640928115 1
+60 1 0.92486376 0.8345901 0.26086032665918085 1
+63 1 0.628109 0.6863574 0.54296812694597407 1
+64 0 -1.3824838 0.0075644017 0.010954609816905481 0
+66 0 -1.356407 0.008135879 0.01178560127249138 0
+68 1 2.7350605 0.998788 0.0017496080888680819 1
+69 0 -1.3430442 0.00844512 0.01223547138758322 0
+70 0 -1.3274422 0.00882095 0.012782400606890431 0
+71 1 2.5088794 0.9977114 0.0033055068339777546 1
+72 0 -1.0962008 0.016777162 0.024409667586753104 0
+73 1 2.0166745 0.99091476 0.013167132026296508 1
+74 1 0.89674574 0.8233718 0.28038400847454559 1
+76 0 -1.3025879 0.009454169 0.01370436744687141 0
+77 0 -1.372789 0.0077720587 0.01125651057865878 0
+79 0 -1.3969742 0.00726425 0.010518347766608642 0
+82 0 -1.3485314 0.008316752 0.012048709045009985 0
+88 0 -1.356407 0.008135879 0.01178560127249138 0
+90 0 -1.3541391 0.008187561 0.011860776025037548 0
+91 0 -1.4125189 0.006955395 0.010069573522678576 0
+92 0 -1.356407 0.008135879 0.01178560127249138 0
+93 0 -1.3824838 0.0075644017 0.010954609816905481 0
+95 0 -1.3541391 0.008187561 0.011860776025037548 0
+96 0 -1.407162 0.00706033 0.010222030995856075 0
+97 0 -1.3535177 0.00820178 0.011881458472842533 0
+98 1 2.460168 0.99737597 0.0037906576977285817 1
+99 1 2.5687213 0.99806553 0.0027935513577779722 1
+100 1 1.5606362 0.9679606 0.046979775683377911 1
+102 0 -1.3243545 0.008897268 0.012893488577931858 0
+104 1 3.3844311 0.999805 0.00028139105704564227 1
+105 1 1.0054644 0.86358684 0.21158683122054089 1
+106 1 2.0241597 0.9911025 0.012893801739185438 1
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diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..4aa31718a3
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,38 @@
+maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+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): 0.880772
+AUC: 0.996136
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.996136 (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: 0.880772 (0.0000)
+F1 Score: 0.962656 (0.0000)
+AUPRC: 0.992151 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..b6a73dfb8d
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+LdSvm
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.996136 0.973646 0.954733 0.970711 0.984091 0.975225 0.111359 0.880772 0.962656 0.992151 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000} dout=%Output% data=%Data% out=%Output% seed=1 /iter:1000
+
diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..e08f9f95c4
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
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diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer-out.txt
new file mode 100644
index 0000000000..8ec2c1eb21
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer-out.txt
@@ -0,0 +1,56 @@
+maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% 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 || 127 | 7 | 0.9478
+ negative || 9 | 211 | 0.9591
+ ||======================
+Precision || 0.9338 | 0.9679 |
+OVERALL 0/1 ACCURACY: 0.954802
+LOG LOSS/instance: 0.251296
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): 0.737412
+AUC: 0.983989
+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 || 18 | 206 | 0.9196
+ ||======================
+Precision || 0.8475 | 0.9763 |
+OVERALL 0/1 ACCURACY: 0.930091
+LOG LOSS/instance: 0.240850
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.733412
+AUC: 0.973257
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.978623 (0.0054)
+Accuracy: 0.942447 (0.0124)
+Positive precision: 0.890641 (0.0432)
+Positive recall: 0.950071 (0.0023)
+Negative precision: 0.972097 (0.0042)
+Negative recall: 0.939367 (0.0197)
+Log-loss: 0.246073 (0.0052)
+Log-loss reduction: 0.735412 (0.0020)
+F1 Score: 0.918801 (0.0219)
+AUPRC: 0.970562 (0.0033)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer-rp.txt
new file mode 100644
index 0000000000..3426abd1d9
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+LdSvm
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.978623 0.942447 0.890641 0.950071 0.972097 0.939367 0.246073 0.735412 0.918801 0.970562 - 1000 LdSvm %Data% %Output% 99 0 0 maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=%Output% data=%Data% seed=1 /bias:-;/iter:1000
+
diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer.txt
new file mode 100644
index 0000000000..f6ee9c28ed
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-CV-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
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diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..8aa452acde
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,38 @@
+maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+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 || 229 | 10 | 0.9582
+ negative || 16 | 428 | 0.9640
+ ||======================
+Precision || 0.9347 | 0.9772 |
+OVERALL 0/1 ACCURACY: 0.961933
+LOG LOSS/instance: 0.188051
+Test-set entropy (prior Log-Loss/instance): 0.934003
+LOG-LOSS REDUCTION (RIG): 0.798662
+AUC: 0.984121
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.984121 (0.0000)
+Accuracy: 0.961933 (0.0000)
+Positive precision: 0.934694 (0.0000)
+Positive recall: 0.958159 (0.0000)
+Negative precision: 0.977169 (0.0000)
+Negative recall: 0.963964 (0.0000)
+Log-loss: 0.188051 (0.0000)
+Log-loss reduction: 0.798662 (0.0000)
+F1 Score: 0.946281 (0.0000)
+AUPRC: 0.975440 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..2771c11f23
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+LdSvm
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /bias /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.984121 0.961933 0.934694 0.958159 0.977169 0.963964 0.188051 0.798662 0.946281 0.97544 - 1000 LdSvm %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=LdSvm{iter=1000 bias=-} dout=%Output% data=%Data% out=%Output% seed=1 /bias:-;/iter:1000
+
diff --git a/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..c8b2b99594
--- /dev/null
+++ b/test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-nob-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
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diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris-out.txt
new file mode 100644
index 0000000000..f0bf9e77bf
--- /dev/null
+++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris-out.txt
@@ -0,0 +1,61 @@
+maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% 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 12 of 15 weights.
+Trainer 1 of 5 finished in %Time%
+Beginning training model 2 of 5
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 13 of 15 weights.
+Trainer 2 of 5 finished in %Time%
+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 %Time%
+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 %Time%
+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 %Time%
+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: 0.433907
+Log-loss reduction: 0.605040
+
+OVERALL RESULTS
+---------------------------------------
+Accuracy(micro-avg): 0.940000 (0.0000)
+Accuracy(macro-avg): 0.940000 (0.0000)
+Log-loss: 0.433907 (0.0000)
+Log-loss reduction: 0.605040 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris-rp.txt
new file mode 100644
index 0000000000..d89b06d6ec
--- /dev/null
+++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris-rp.txt
@@ -0,0 +1,4 @@
+WeightedEnsembleMulticlass
+Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.94 0.94 0.433907 0.60504 MultiAverage mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiAverage;/bp:mlr{t-};/nm:5
+
diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris.txt
new file mode 100644
index 0000000000..fde0e39ddc
--- /dev/null
+++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Average-TrainTest-iris.txt
@@ -0,0 +1,151 @@
+Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class
+0 0 0 0.20053972614348203 0.818289 0.17061268 0.011098298 0 1 2
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diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris-out.txt
new file mode 100644
index 0000000000..2da2bb56f1
--- /dev/null
+++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris-out.txt
@@ -0,0 +1,151 @@
+maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=%Output% data=%Data% out=%Output% 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 %Time%
+Beginning training model 2 of 20
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 13 of 15 weights.
+Trainer 2 of 20 finished in %Time%
+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 %Time%
+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 %Time%
+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 %Time%
+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 %Time%
+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 %Time%
+Beginning training model 8 of 20
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 11 of 15 weights.
+Trainer 8 of 20 finished in %Time%
+Beginning training model 9 of 20
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 12 of 15 weights.
+Trainer 9 of 20 finished in %Time%
+Beginning training model 10 of 20
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 12 of 15 weights.
+Trainer 10 of 20 finished in %Time%
+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 %Time%
+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 %Time%
+Beginning training model 13 of 20
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 11 of 15 weights.
+Trainer 13 of 20 finished in %Time%
+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 %Time%
+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 %Time%
+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 %Time%
+Beginning training model 17 of 20
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 11 of 15 weights.
+Trainer 17 of 20 finished in %Time%
+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 %Time%
+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 %Time%
+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 %Time%
+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: 0.435480
+Log-loss reduction: 0.603609
+
+OVERALL RESULTS
+---------------------------------------
+Accuracy(micro-avg): 0.940000 (0.0000)
+Accuracy(macro-avg): 0.940000 (0.0000)
+Log-loss: 0.435480 (0.0000)
+Log-loss reduction: 0.603609 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris-rp.txt
new file mode 100644
index 0000000000..d6cdd0978b
--- /dev/null
+++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris-rp.txt
@@ -0,0 +1,4 @@
+WeightedEnsembleMulticlass
+Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.94 0.94 0.43548 0.603609 mlr{t-} 20 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /bp:mlr{t-};/nm:20
+
diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris.txt
new file mode 100644
index 0000000000..8423797f83
--- /dev/null
+++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Bootstrap-TrainTest-iris.txt
@@ -0,0 +1,151 @@
+Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class
+0 0 0 0.19152118697043422 0.82570213 0.16328812 0.0113295 0 1 2
+1 0 0 0.21068143989807603 0.81003207 0.1779941 0.011699337 0 1 2
+2 0 0 0.1939901995522916 0.823666 0.16582173 0.010781916 0 1 2
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+10 0 0 0.19237385779646321 0.8249984 0.16319379 0.011959545 0 1 2
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+26 0 0 0.27049738893648129 0.7629999 0.21738029 0.02019529 0 1 2
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+149 2 2 0.57133800640125854 0.56476927 0.39011377 0.046327032 2 1 0
diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris-out.txt
new file mode 100644
index 0000000000..3d92491bd9
--- /dev/null
+++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris-out.txt
@@ -0,0 +1,67 @@
+maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-} dout=%Output% data=%Data% out=%Output% 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 %Time%
+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 %Time%
+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 %Time%
+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 %Time%
+Beginning training model 5 of 5
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 11 of 15 weights.
+Trainer 5 of 5 finished in %Time%
+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: 0.431192
+Log-loss reduction: 0.607512
+
+OVERALL RESULTS
+---------------------------------------
+Accuracy(micro-avg): 0.900000 (0.0000)
+Accuracy(macro-avg): 0.900000 (0.0000)
+Log-loss: 0.431192 (0.0000)
+Log-loss reduction: 0.607512 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris-rp.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris-rp.txt
new file mode 100644
index 0000000000..86f33013f2
--- /dev/null
+++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris-rp.txt
@@ -0,0 +1,4 @@
+WeightedEnsembleMulticlass
+Accuracy(micro-avg) Accuracy(macro-avg) Log-loss Log-loss reduction /oc /bp /nm Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.9 0.9 0.431192 0.607512 MultiStacking{bp=mlr{t-}} mlr{t-} 5 WeightedEnsembleMulticlass %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-} dout=%Output% data=%Data% out=%Output% seed=1 xf=Term{col=Label} /oc:MultiStacking{bp=mlr{t-}};/bp:mlr{t-};/nm:5
+
diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris.txt
new file mode 100644
index 0000000000..0984766046
--- /dev/null
+++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Stacking-TrainTest-iris.txt
@@ -0,0 +1,151 @@
+Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class
+0 0 0 0.18417831910726618 0.83178747 0.12211669 0.046095874 0 1 2
+1 0 0 0.20085838160953873 0.8180283 0.13298129 0.04899046 0 1 2
+2 0 0 0.18784067589144673 0.82874674 0.12464202 0.046611313 0 1 2
+3 0 0 0.20316170657415891 0.81614625 0.13436139 0.049492422 0 1 2
+4 0 0 0.18101980453427502 0.83441883 0.12005545 0.045525637 0 1 2
+5 0 0 0.23063537278625518 0.79402894 0.15011322 0.055857826 0 1 2
+6 0 0 0.20551039063434751 0.81423163 0.13550417 0.050264206 0 1 2
+7 0 0 0.19294384173028295 0.8245283 0.12771659 0.047755092 0 1 2
+8 0 0 0.2039968819483334 0.8154649 0.13504232 0.049492884 0 1 2
+9 0 0 0.18528605629437742 0.8308666 0.123129375 0.046004 0 1 2
+10 0 0 0.1836559933970561 0.83222204 0.12164412 0.046133816 0 1 2
+11 0 0 0.19874857882922087 0.819756 0.13136268 0.0488813 0 1 2
+12 0 0 0.18299221467912113 0.83277464 0.12172645 0.04549903 0 1 2
+13 0 0 0.1680424386321698 0.84531796 0.11212196 0.042560056 0 1 2
+14 0 0 0.16163994405123333 0.85074747 0.10744801 0.04180455 0 1 2
+15 0 0 0.19709491663980536 0.8211127 0.12925051 0.0496367 0 1 2
+16 0 0 0.20169710407147379 0.81734246 0.13257436 0.050083205 0 1 2
+17 0 0 0.20239261219976509 0.8167742 0.13346308 0.049762588 0 1 2
+18 0 0 0.21185371635262243 0.80908304 0.13898246 0.051934607 0 1 2
+19 0 0 0.1979214785598355 0.8204343 0.13043174 0.04913397 0 1 2
+20 0 0 0.20585465297003774 0.8139514 0.13576454 0.050283995 0 1 2
+21 0 0 0.2232208019175897 0.7999382 0.14592487 0.054136835 0 1 2
+22 0 0 0.16204666370451035 0.8504015 0.107930906 0.041667618 0 1 2
+23 0 0 0.29264784539139221 0.7462849 0.18700585 0.066709325 0 1 2
+24 0 0 0.2189951016973409 0.80332565 0.14389618 0.052778047 0 1 2
+25 0 0 0.21426487282778184 0.80713457 0.14134 0.051525462 0 1 2
+26 0 0 0.24516770891628276 0.7825733 0.15946501 0.057961766 0 1 2
+27 0 0 0.18981932051504713 0.82710856 0.12567313 0.0472183 0 1 2
+28 0 0 0.18744468302910022 0.829075 0.1242443 0.04668062 0 1 2
+29 0 0 0.20600844517861241 0.8138262 0.1360556 0.050118297 0 1 2
+30 0 0 0.21000477824371253 0.8105804 0.13861993 0.050799653 0 1 2
+31 0 0 0.23720575096014923 0.78882897 0.15468813 0.05648279 0 1 2
+32 0 0 0.15846273328059832 0.85345477 0.10541639 0.04112876 0 1 2
+33 0 0 0.16503070763768193 0.84786767 0.109519124 0.042613126 0 1 2
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+35 0 0 0.18271876949968671 0.8330024 0.121379904 0.045617804 0 1 2
+36 0 0 0.17932179574787918 0.8358369 0.11902391 0.045139056 0 1 2
+37 0 0 0.18528605629437742 0.8308666 0.123129375 0.046004 0 1 2
+38 0 0 0.1942257755959706 0.82347196 0.12882204 0.047705885 0 1 2
+39 0 0 0.19305228169213448 0.82443887 0.12778112 0.04777995 0 1 2
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+41 0 0 0.24628041198592227 0.781703 0.16158472 0.05671222 0 1 2
+42 0 0 0.18753958622431757 0.8289963 0.124460906 0.04654281 0 1 2
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+44 0 0 0.25233363344804022 0.77698547 0.16309705 0.05991737 0 1 2
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+46 0 0 0.18566633959997436 0.8305507 0.12285163 0.04659769 0 1 2
+47 0 0 0.19345281624008642 0.8241087 0.12819196 0.047699243 0 1 2
+48 0 0 0.18355558567861183 0.8323056 0.121584095 0.04611024 0 1 2
+49 0 0 0.19050668570707108 0.82654023 0.12625176 0.047208056 0 1 2
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diff --git a/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Voting-TrainTest-iris-out.txt b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Voting-TrainTest-iris-out.txt
new file mode 100644
index 0000000000..cec0c9958b
--- /dev/null
+++ b/test/BaselineOutput/Common/WeightedEnsembleMulticlass/netcoreapp31/WE-Voting-TrainTest-iris-out.txt
@@ -0,0 +1,61 @@
+maml.exe TrainTest test=%Data% tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiVoting tp=-} dout=%Output% data=%Data% out=%Output% 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 12 of 15 weights.
+Trainer 1 of 5 finished in %Time%
+Beginning training model 2 of 5
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 13 of 15 weights.
+Trainer 2 of 5 finished in %Time%
+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 %Time%
+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 %Time%
+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 %Time%
+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): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt
new file mode 100644
index 0000000000..c24820c51d
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt
@@ -0,0 +1,58 @@
+maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% 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 (over 1 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 8 instances with missing features during training (over 1 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 || 132 | 2 | 0.9851
+ negative || 9 | 211 | 0.9591
+ ||======================
+Precision || 0.9362 | 0.9906 |
+OVERALL 0/1 ACCURACY: 0.968927
+LOG LOSS/instance: Infinity
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): -Infinity
+AUC: 0.992809
+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 || 102 | 3 | 0.9714
+ negative || 4 | 220 | 0.9821
+ ||======================
+Precision || 0.9623 | 0.9865 |
+OVERALL 0/1 ACCURACY: 0.978723
+LOG LOSS/instance: 0.239330
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.735095
+AUC: 0.997279
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.995044 (0.0022)
+Accuracy: 0.973825 (0.0049)
+Positive precision: 0.949217 (0.0130)
+Positive recall: 0.978252 (0.0068)
+Negative precision: 0.988579 (0.0020)
+Negative recall: 0.970617 (0.0115)
+Log-loss: Infinity (NaN)
+Log-loss reduction: -Infinity (NaN)
+F1 Score: 0.963412 (0.0034)
+AUPRC: 0.990159 (0.0038)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt
new file mode 100644
index 0000000000..aaf6943c00
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt
@@ -0,0 +1,4 @@
+AveragedPerceptron
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.995044 0.973825 0.949217 0.978252 0.988579 0.970617 Infinity -Infinity 0.963412 0.990159 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1
+
diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt
new file mode 100644
index 0000000000..401503d4e9
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
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+40 0 ? ? ? 0
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diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt
new file mode 100644
index 0000000000..b683beedd9
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt
@@ -0,0 +1,56 @@
+maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=%Output% data=%Data% 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 (over 1 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 8 instances with missing features during training (over 1 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 || 9 | 211 | 0.9591
+ ||======================
+Precision || 0.9362 | 0.9906 |
+OVERALL 0/1 ACCURACY: 0.968927
+LOG LOSS/instance: 0.155883
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): 0.837113
+AUC: 0.992809
+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 || 102 | 3 | 0.9714
+ negative || 4 | 220 | 0.9821
+ ||======================
+Precision || 0.9623 | 0.9865 |
+OVERALL 0/1 ACCURACY: 0.978723
+LOG LOSS/instance: 0.130063
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.856038
+AUC: 0.997279
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.995044 (0.0022)
+Accuracy: 0.973825 (0.0049)
+Positive precision: 0.949217 (0.0130)
+Positive recall: 0.978252 (0.0068)
+Negative precision: 0.988579 (0.0020)
+Negative recall: 0.970617 (0.0115)
+Log-loss: 0.142973 (0.0129)
+Log-loss reduction: 0.846576 (0.0095)
+F1 Score: 0.963412 (0.0034)
+AUPRC: 0.990159 (0.0038)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt
new file mode 100644
index 0000000000..2b08b6ae55
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt
@@ -0,0 +1,4 @@
+AveragedPerceptron
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.995044 0.973825 0.949217 0.978252 0.988579 0.970617 0.142973 0.846576 0.963412 0.990159 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=%Output% data=%Data% seed=1
+
diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt
new file mode 100644
index 0000000000..3657243035
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
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+40 0 ? ? ? 0
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diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt
new file mode 100644
index 0000000000..11352e4215
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt
@@ -0,0 +1,58 @@
+maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=%Output% data=%Data% 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 (over 1 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 8 instances with missing features during training (over 1 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 || 132 | 2 | 0.9851
+ negative || 9 | 211 | 0.9591
+ ||======================
+Precision || 0.9362 | 0.9906 |
+OVERALL 0/1 ACCURACY: 0.968927
+LOG LOSS/instance: NaN
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): 0.000000
+AUC: 0.992809
+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 || 102 | 3 | 0.9714
+ negative || 4 | 220 | 0.9821
+ ||======================
+Precision || 0.9623 | 0.9865 |
+OVERALL 0/1 ACCURACY: 0.978723
+LOG LOSS/instance: NaN
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.000000
+AUC: 0.997279
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.995044 (0.0022)
+Accuracy: 0.973825 (0.0049)
+Positive precision: 0.949217 (0.0130)
+Positive recall: 0.978252 (0.0068)
+Negative precision: 0.988579 (0.0020)
+Negative recall: 0.970617 (0.0115)
+Log-loss: NaN (NaN)
+Log-loss reduction: 0.000000 (0.0000)
+F1 Score: 0.963412 (0.0034)
+AUPRC: 0.990159 (0.0038)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt
new file mode 100644
index 0000000000..c8b40ed142
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt
@@ -0,0 +1,4 @@
+AveragedPerceptron
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.995044 0.973825 0.949217 0.978252 0.988579 0.970617 NaN 0 0.963412 0.990159 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=%Output% data=%Data% seed=1
+
diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration.txt
new file mode 100644
index 0000000000..fda1ad7cbf
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration.txt
@@ -0,0 +1,700 @@
+Instance Label Score Assigned
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diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt
new file mode 100644
index 0000000000..4e70ed0ceb
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt
new file mode 100644
index 0000000000..257962463f
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt
new file mode 100644
index 0000000000..a39a23abdd
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-out.txt
new file mode 100644
index 0000000000..6d166f9706
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-out.txt
@@ -0,0 +1,121 @@
+maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} threads=- norm=No dout=%Output% data=%Data% 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: 0.111875
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.876170
+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: 0.126419 (0.0145)
+Log-loss reduction: 0.864436 (0.0117)
+F1 Score: 0.946603 (0.0054)
+AUPRC: 0.991761 (0.0020)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'LBFGS data prep' started.
+[1] 'LBFGS data prep' finished in %Time%.
+[2] 'LBFGS Optimizer' started.
+[2] (%Time%) 0 iterations Loss: 0.6931471824645996
+[2] (%Time%) 1 iterations Loss: 0.6544521450996399 Improvement: 0.0387
+[2] (%Time%) 2 iterations Loss: 0.6425679922103882 Improvement: 0.01725
+[2] (%Time%) 3 iterations Loss: 0.6358204483985901 Improvement: 0.009247
+[2] (%Time%) 4 iterations Loss: 0.6251664161682129 Improvement: 0.01031
+[2] (%Time%) 5 iterations Loss: 0.5822217464447021 Improvement: 0.03481
+[2] (%Time%) 6 iterations Loss: 0.2409491091966629 Improvement: 0.2647
+[2] (%Time%) 7 iterations Loss: 0.18065251410007477 Improvement: 0.1114
+[2] (%Time%) 8 iterations Loss: 0.12022750824689865 Improvement: 0.07317
+[2] (%Time%) 9 iterations Loss: 0.09626178443431854 Improvement: 0.03627
+[2] (%Time%) 10 iterations Loss: 0.08480900526046753 Improvement: 0.01766
+[2] (%Time%) 11 iterations Loss: 0.07748782634735107 Improvement: 0.009905
+[2] (%Time%) 12 iterations Loss: 0.07508619874715805 Improvement: 0.004277
+[2] (%Time%) 13 iterations Loss: 0.07309562712907791 Improvement: 0.002562
+[2] (%Time%) 14 iterations Loss: 0.07258497178554535 Improvement: 0.001024
+[2] (%Time%) 15 iterations Loss: 0.07184841483831406 Improvement: 0.0008083
+[2] (%Time%) 16 iterations Loss: 0.07156649976968765 Improvement: 0.0004135
+[2] (%Time%) 17 iterations Loss: 0.0715627521276474 Improvement: 0.0001062
+[2] (%Time%) 18 iterations Loss: 0.07146956771612167 Improvement: 9.644E-05
+[2] 'LBFGS Optimizer' finished in %Time%.
+[3] 'LBFGS data prep #2' started.
+[3] 'LBFGS data prep #2' finished in %Time%.
+[4] 'LBFGS Optimizer #2' started.
+[4] (%Time%) 0 iterations Loss: 0.6931471824645996
+[4] (%Time%) 1 iterations Loss: 0.6328520774841309 Improvement: 0.0603
+[4] (%Time%) 2 iterations Loss: 0.620979368686676 Improvement: 0.02156
+[4] (%Time%) 3 iterations Loss: 0.613497793674469 Improvement: 0.01083
+[4] (%Time%) 4 iterations Loss: 0.6002617478370667 Improvement: 0.01264
+[4] (%Time%) 5 iterations Loss: 0.5936987996101379 Improvement: 0.008078
+[4] (%Time%) 6 iterations Loss: 0.5855146050453186 Improvement: 0.008158
+[4] (%Time%) 7 iterations Loss: 0.557903528213501 Improvement: 0.02275
+[4] (%Time%) 8 iterations Loss: 0.514011561870575 Improvement: 0.03861
+[4] (%Time%) 9 iterations Loss: 0.47046783566474915 Improvement: 0.04231
+[4] (%Time%) 10 iterations Loss: 0.3519411087036133 Improvement: 0.09947
+[4] (%Time%) 11 iterations Loss: 0.24160850048065186 Improvement: 0.1076
+[4] (%Time%) 12 iterations Loss: 0.15622486174106598 Improvement: 0.09094
+[4] (%Time%) 13 iterations Loss: 0.13706204295158386 Improvement: 0.03711
+[4] (%Time%) 14 iterations Loss: 0.12257859110832214 Improvement: 0.02014
+[4] (%Time%) 15 iterations Loss: 0.112119160592556 Improvement: 0.01288
+[4] (%Time%) 16 iterations Loss: 0.10575493425130844 Improvement: 0.007993
+[4] (%Time%) 17 iterations Loss: 0.10021665692329407 Improvement: 0.006152
+[4] (%Time%) 18 iterations Loss: 0.09684965759515762 Improvement: 0.004063
+[4] (%Time%) 19 iterations Loss: 0.09285992383956909 Improvement: 0.004008
+[4] (%Time%) 20 iterations Loss: 0.09131418168544769 Improvement: 0.002161
+[4] (%Time%) 21 iterations Loss: 0.09053913503885269 Improvement: 0.001122
+[4] (%Time%) 22 iterations Loss: 0.09002908319234848 Improvement: 0.0006629
+[4] (%Time%) 23 iterations Loss: 0.08863696455955505 Improvement: 0.00121
+[4] (%Time%) 24 iterations Loss: 0.08813063055276871 Improvement: 0.0006822
+[4] (%Time%) 25 iterations Loss: 0.0875096321105957 Improvement: 0.0006363
+[4] (%Time%) 26 iterations Loss: 0.08728791028261185 Improvement: 0.0003254
+[4] (%Time%) 27 iterations Loss: 0.08721961826086044 Improvement: 0.0001326
+[4] (%Time%) 28 iterations Loss: 0.08721006661653519 Improvement: 4.03E-05
+[4] 'LBFGS Optimizer #2' finished in %Time%.
diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-rp.txt
new file mode 100644
index 0000000000..5a076145d7
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+LogisticRegression
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt /nn Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.995752 0.963401 0.955651 0.938486 0.968914 0.975122 0.126419 0.864436 0.946603 0.991761 0.1 0.0001 1 + LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} threads=- norm=No dout=%Output% data=%Data% seed=1 /l2:0.1;/ot:0.0001;/nt:1;/nn:+
+
diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer.txt
new file mode 100644
index 0000000000..26fdb16999
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
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+40 0 ? ? ? 0
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diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..af2726b69b
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,71 @@
+maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} norm=No dout=%Output% data=%Data% out=%Output% 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): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'LBFGS data prep' started.
+[1] 'LBFGS data prep' finished in %Time%.
+[2] 'LBFGS Optimizer' started.
+[2] (%Time%) 0 iterations Loss: 0.6931471824645996
+[2] (%Time%) 1 iterations Loss: 0.6448327898979187 Improvement: 0.04831
+[2] (%Time%) 2 iterations Loss: 0.6306551098823547 Improvement: 0.02101
+[2] (%Time%) 3 iterations Loss: 0.6191107034683228 Improvement: 0.0138
+[2] (%Time%) 4 iterations Loss: 0.6139997243881226 Improvement: 0.007257
+[2] (%Time%) 5 iterations Loss: 0.6017452478408813 Improvement: 0.01101
+[2] (%Time%) 6 iterations Loss: 0.5552629232406616 Improvement: 0.03762
+[2] (%Time%) 7 iterations Loss: 0.40808025002479553 Improvement: 0.1198
+[2] (%Time%) 8 iterations Loss: 0.22476567327976227 Improvement: 0.1674
+[2] (%Time%) 9 iterations Loss: 0.17795756459236145 Improvement: 0.07696
+[2] (%Time%) 10 iterations Loss: 0.13737377524375916 Improvement: 0.04968
+[2] (%Time%) 11 iterations Loss: 0.11691863089799881 Improvement: 0.02776
+[2] (%Time%) 12 iterations Loss: 0.10186515003442764 Improvement: 0.01823
+[2] (%Time%) 13 iterations Loss: 0.09181062132120132 Improvement: 0.0121
+[2] (%Time%) 14 iterations Loss: 0.08791223913431168 Improvement: 0.005948
+[2] (%Time%) 15 iterations Loss: 0.08431486040353775 Improvement: 0.004185
+[2] (%Time%) 16 iterations Loss: 0.08275849372148514 Improvement: 0.002214
+[2] (%Time%) 17 iterations Loss: 0.08134783804416656 Improvement: 0.001611
+[2] (%Time%) 18 iterations Loss: 0.08036927133798599 Improvement: 0.001137
+[2] (%Time%) 19 iterations Loss: 0.07970956712961197 Improvement: 0.000779
+[2] (%Time%) 20 iterations Loss: 0.07952727377414703 Improvement: 0.0003315
+[2] (%Time%) 21 iterations Loss: 0.07951106876134872 Improvement: 9.502E-05
+[2] 'LBFGS Optimizer' finished in %Time%.
+[3] 'Saving model' started.
+[3] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..5e638a505e
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
+0 0 -3.9978461 0.018024292 0.026240758896432691 0
+1 0 2.257082 0.90525967 3.3998774747825071 1
+2 0 -4.6860514 0.009138746 0.013245037929465322 0
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+4 0 -3.8983483 0.019872451 0.028958588469894953 0
+5 1 10.383743 0.99996907 4.4630188215668621E-05 1
+6 0 -2.678063 0.064280294 0.095851658190808028 0
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+14 1 8.055382 0.9996827 0.00045780445418641197 1
+15 1 0.7239504 0.67347634 0.57030083530530062 1
+16 0 -4.965964 0.006922967 0.010022462627968663 0
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+18 1 6.5412807 0.9985594 0.0020798227846953346 1
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+20 1 6.5972366 0.99863774 0.001966671610202825 1
+21 1 6.909416 0.99900264 0.0014396108762453246 1
+22 0 -5.502652 0.004059401 0.0058683966738597888 0
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+695 0 -6.4707704 0.00154564 0.0022316123258321369 0
+696 1 5.1025305 0.99395543 0.0087469287128904821 1
+697 1 3.3554373 0.9662824 0.049483169875034191 1
+698 1 4.277025 0.98630625 0.019892418981030672 1
diff --git a/test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris-out.txt b/test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris-out.txt
new file mode 100644
index 0000000000..6f8b64ff3c
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris-out.txt
@@ -0,0 +1,111 @@
+maml.exe CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=%Output% data=%Data% seed=1 xf=Term{col=Label}
+Not adding a normalizer.
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 15 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 15 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: 0.101866
+Log-loss reduction: 0.906331
+
+Confusion table
+ ||========================
+PREDICTED || 0 | 1 | 2 | Recall
+TRUTH ||========================
+ 0 || 29 | 0 | 0 | 1.0000
+ 1 || 0 | 19 | 1 | 0.9500
+ 2 || 0 | 0 | 22 | 1.0000
+ ||========================
+Precision ||1.0000 |1.0000 |0.9565 |
+Accuracy(micro-avg): 0.985915
+Accuracy(macro-avg): 0.983333
+Log-loss: 0.075796
+Log-loss reduction: 0.930183
+
+OVERALL RESULTS
+---------------------------------------
+Accuracy(micro-avg): 0.973970 (0.0119)
+Accuracy(macro-avg): 0.974206 (0.0091)
+Log-loss: 0.088831 (0.0130)
+Log-loss reduction: 0.918257 (0.0119)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'Building term dictionary' started.
+[1] (%Time%) 71 examples Total Terms: 3
+[1] 'Building term dictionary' finished in %Time%.
+[2] 'LBFGS data prep' started.
+[2] 'LBFGS data prep' finished in %Time%.
+[3] 'LBFGS Optimizer' started.
+[3] (%Time%) 0 iterations Loss: 1.0986123085021973
+[3] (%Time%) 1 iterations Loss: 0.9755012392997742 Improvement: 0.1231
+[3] (%Time%) 2 iterations Loss: 0.8284684419631958 Improvement: 0.1422
+[3] (%Time%) 3 iterations Loss: 0.49238577485084534 Improvement: 0.2899
+[3] (%Time%) 4 iterations Loss: 0.4102630317211151 Improvement: 0.1335
+[3] (%Time%) 5 iterations Loss: 0.3732020854949951 Improvement: 0.06109
+[3] (%Time%) 6 iterations Loss: 0.3262292444705963 Improvement: 0.0505
+[3] (%Time%) 7 iterations Loss: 0.30860307812690735 Improvement: 0.02584
+[3] (%Time%) 8 iterations Loss: 0.24991190433502197 Improvement: 0.05048
+[3] (%Time%) 9 iterations Loss: 0.19703027606010437 Improvement: 0.05228
+[3] (%Time%) 10 iterations Loss: 0.1837688386440277 Improvement: 0.02302
+[3] (%Time%) 11 iterations Loss: 0.17426884174346924 Improvement: 0.01288
+[3] (%Time%) 12 iterations Loss: 0.14890950918197632 Improvement: 0.02224
+[3] (%Time%) 13 iterations Loss: 0.14668036997318268 Improvement: 0.007232
+[3] (%Time%) 14 iterations Loss: 0.12762901186943054 Improvement: 0.0161
+[3] (%Time%) 15 iterations Loss: 0.12740232050418854 Improvement: 0.004194
+[3] (%Time%) 16 iterations Loss: 0.12709592282772064 Improvement: 0.001278
+[3] (%Time%) 17 iterations Loss: 0.12680405378341675 Improvement: 0.0005385
+[3] 'LBFGS Optimizer' finished in %Time%.
+[4] 'Building term dictionary #2' started.
+[4] (%Time%) 79 examples Total Terms: 3
+[4] 'Building term dictionary #2' finished in %Time%.
+[5] 'LBFGS data prep #2' started.
+[5] 'LBFGS data prep #2' finished in %Time%.
+[6] 'LBFGS Optimizer #2' started.
+[6] (%Time%) 0 iterations Loss: 1.0986123085021973
+[6] (%Time%) 1 iterations Loss: 1.036556363105774 Improvement: 0.06206
+[6] (%Time%) 2 iterations Loss: 1.0036184787750244 Improvement: 0.03876
+[6] (%Time%) 3 iterations Loss: 0.9370792508125305 Improvement: 0.05993
+[6] (%Time%) 4 iterations Loss: 0.8192444443702698 Improvement: 0.1035
+[6] (%Time%) 5 iterations Loss: 0.728320837020874 Improvement: 0.09407
+[6] (%Time%) 6 iterations Loss: 0.5819926261901855 Improvement: 0.1333
+[6] (%Time%) 7 iterations Loss: 0.4406244158744812 Improvement: 0.1393
+[6] (%Time%) 8 iterations Loss: 0.36817970871925354 Improvement: 0.08917
+[6] (%Time%) 9 iterations Loss: 0.28754767775535583 Improvement: 0.08277
+[6] (%Time%) 10 iterations Loss: 0.23988358676433563 Improvement: 0.05644
+[6] (%Time%) 11 iterations Loss: 0.21770016849040985 Improvement: 0.03075
+[6] (%Time%) 12 iterations Loss: 0.20622937381267548 Improvement: 0.01629
+[6] (%Time%) 13 iterations Loss: 0.1928335726261139 Improvement: 0.01412
+[6] (%Time%) 14 iterations Loss: 0.18503741919994354 Improvement: 0.009377
+[6] (%Time%) 15 iterations Loss: 0.1817353218793869 Improvement: 0.004821
+[6] (%Time%) 16 iterations Loss: 0.16841556131839752 Improvement: 0.0112
+[6] (%Time%) 17 iterations Loss: 0.1592421531677246 Improvement: 0.009679
+[6] (%Time%) 18 iterations Loss: 0.15063226222991943 Improvement: 0.008877
+[6] (%Time%) 19 iterations Loss: 0.14185991883277893 Improvement: 0.008799
+[6] (%Time%) 20 iterations Loss: 0.1356329470872879 Improvement: 0.00687
+[6] (%Time%) 21 iterations Loss: 0.13491712510585785 Improvement: 0.002254
+[6] (%Time%) 22 iterations Loss: 0.1332564502954483 Improvement: 0.001809
+[6] (%Time%) 23 iterations Loss: 0.13261565566062927 Improvement: 0.0009329
+[6] 'LBFGS Optimizer #2' finished in %Time%.
diff --git a/test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris.txt b/test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris.txt
new file mode 100644
index 0000000000..a077f1e9b6
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris.txt
@@ -0,0 +1,151 @@
+Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class
+5 0 0 0.0039186265649996796 0.99608904 0.003910809 2.0865299E-11 0 1 2
+6 0 0 0.0066394791182328066 0.9933825 0.006617098 2.152395E-10 0 1 2
+8 0 0 0.025494595703479419 0.97482765 0.025172155 2.838915E-09 0 1 2
+9 0 0 0.019190772567240676 0.9809922 0.01900817 2.7058017E-10 0 1 2
+10 0 0 0.0035562493991592786 0.99645007 0.003550249 4.9272335E-12 0 1 2
+11 0 0 0.012517929979546309 0.9875601 0.0124394335 2.8008076E-10 0 1 2
+18 0 0 0.0047769616990695207 0.99523443 0.004765985 8.127231E-12 0 1 2
+20 0 0 0.014707546996037419 0.9854001 0.014600216 8.914893E-11 0 1 2
+21 0 0 0.003647833406238951 0.9963588 0.003641376 3.5084515E-11 0 1 2
+25 0 0 0.032317619176197167 0.968199 0.03180108 1.10023E-09 0 1 2
+28 0 0 0.0057745087199158104 0.99424213 0.0057580057 2.0235997E-11 0 1 2
+31 0 0 0.0071621105155360203 0.9928635 0.0071367947 6.242954E-11 0 1 2
+32 0 0 0.0014066451993830036 0.99859434 0.0014055507 7.3242096E-13 0 1 2
+35 0 0 0.0052270768592320212 0.99478656 0.0052133226 2.5026661E-11 0 1 2
+37 0 0 0.019190772567240676 0.9809922 0.01900817 2.7058017E-10 0 1 2
+40 0 0 0.0033063652339419308 0.9966991 0.003300911 1.895811E-11 0 1 2
+41 0 0 0.077088846429646946 0.9258076 0.0741926 3.3343277E-08 0 1 2
+44 0 0 0.010642032182541446 0.9894144 0.0105858175 3.5036918E-10 0 1 2
+45 0 0 0.017220520546649732 0.9829269 0.017073102 8.5334684E-10 0 1 2
+46 0 0 0.0041847046934752278 0.99582404 0.0041763177 1.5150974E-11 0 1 2
+48 0 0 0.0036583023932260659 0.9963484 0.0036516767 6.700264E-12 0 1 2
+50 1 1 0.0098977138531054429 0.9901511 0.005578102 0.0042707166 1 2 0
+51 1 1 0.031642446182477788 0.96885294 0.024089899 0.007057201 1 2 0
+52 1 1 0.03573585794885261 0.9648951 0.033440154 0.001664746 1 2 0
+54 1 1 0.072409637391112461 0.9301498 0.06803778 0.0018123286 1 2 0
+56 1 1 0.093649650619783725 0.91060174 0.08499099 0.0044072797 1 2 0
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+63 1 1 0.137502875229945 0.87153184 0.1271067 0.0013614136 1 2 0
+64 1 1 0.054985501307998973 0.9464989 0.048023954 0.0054772357 1 0 2
+66 1 1 0.32485007538570837 0.7226357 0.27483696 0.0025274218 1 2 0
+68 1 1 0.41495870735351531 0.66036755 0.33927307 0.00035943443 1 2 0
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+100 2 2 0.00061608453557407644 0.9993841 0.0006157393 5.2001777E-08 2 1 0
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+133 2 1 0.80138632910758123 0.5511005 0.44870648 0.00019297059 1 2 0
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+147 2 2 0.1444256224627285 0.8655193 0.13441183 6.885606E-05 2 1 0
+0 0 0 0.0038471219092532103 0.99616027 0.0038398658 4.272304E-13 0 1 2
+1 0 0 0.012569414513148094 0.98750925 0.012490312 1.16851225E-11 0 1 2
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+7 0 0 0.0071029796876616886 0.9929222 0.0070774616 2.3269897E-12 0 1 2
+12 0 0 0.013135919395226348 0.98695 0.013049681 9.69176E-12 0 1 2
+13 0 0 0.006740587102816476 0.9932821 0.006718063 5.392806E-12 0 1 2
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+16 0 0 0.0010137330102610047 0.9989868 0.0010132674 2.4230277E-14 0 1 2
+17 0 0 0.003937715283349302 0.99607 0.0039304383 7.4476986E-13 0 1 2
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+30 0 0 0.021263117789093924 0.97896135 0.021038212 5.088387E-11 0 1 2
+33 0 0 0.00067328225244051433 0.99932694 0.0006728331 2.645334E-15 0 1 2
+34 0 0 0.013916015058125409 0.98618037 0.013819583 9.011755E-12 0 1 2
+36 0 0 0.0020971218736402456 0.9979051 0.0020952104 4.3312952E-14 0 1 2
+38 0 0 0.012599956415221663 0.9874791 0.012520914 3.2850903E-11 0 1 2
+39 0 0 0.0066385190914096566 0.99338347 0.006616723 1.6090942E-12 0 1 2
+42 0 0 0.0082874029946546218 0.99174684 0.008253521 1.1761102E-11 0 1 2
+43 0 0 0.0088386780169330721 0.99120027 0.0087993285 2.8893552E-11 0 1 2
+47 0 0 0.010122275828759237 0.9899288 0.010071042 1.2646444E-11 0 1 2
+49 0 0 0.0062646587177839679 0.9937549 0.0062455637 1.729813E-12 0 1 2
+53 1 1 0.10698867632136459 0.89853585 0.09846105 0.0030030068 1 2 0
+55 1 1 0.13397672143525333 0.8746104 0.12360578 0.0017838533 1 2 0
+57 1 1 0.034013499575166965 0.96655846 0.030011753 0.0034298701 1 0 2
+58 1 1 0.014321588595529325 0.9857805 0.010956884 0.0032627187 1 2 0
+59 1 1 0.089034322664890295 0.9148142 0.07726592 0.007920011 1 2 0
+61 1 1 0.037737148309352063 0.962966 0.028062146 0.008971874 1 2 0
+62 1 1 0.010682094145451054 0.98937476 0.00659236 0.004032912 1 2 0
+65 1 1 0.013174752701392319 0.98691165 0.010195948 0.002892394 1 0 2
+67 1 1 0.011484394147178454 0.9885813 0.007202252 0.0042163264 1 0 2
+75 1 1 0.013111581404427745 0.986974 0.0077194124 0.005306578 1 0 2
+78 1 1 0.11794544915001207 0.88874453 0.108638965 0.0026165938 1 2 0
+80 1 1 0.019020538797954168 0.9811592 0.010528394 0.008312513 1 2 0
+81 1 1 0.015925180791274182 0.98420095 0.011949363 0.0038497108 1 0 2
+83 1 2 2.0406980454630639 0.87002933 0.12993798 3.258634E-05 2 1 0
+84 1 1 0.43994994621280786 0.64406866 0.35437283 0.0015585137 1 2 0
+85 1 1 0.05211593834564536 0.9492188 0.04295896 0.007822319 1 2 0
+86 1 1 0.024243581990507159 0.97604793 0.020361558 0.0035904064 1 2 0
+87 1 1 0.065390833308409307 0.9367013 0.061896957 0.0014018206 1 2 0
+89 1 1 0.062605639482612899 0.9393138 0.055903245 0.0047830124 1 2 0
+94 1 1 0.064166784413406702 0.93784857 0.058178328 0.003973016 1 2 0
+101 2 2 0.016407972136600949 0.9837259 0.016270882 3.321601E-06 2 1 0
+103 2 2 0.021377102120025193 0.97884977 0.021148358 1.7575348E-06 2 1 0
+107 2 2 0.015548256759951539 0.984572 0.015427698 2.396388E-07 2 1 0
+110 2 2 0.31425472469679994 0.730333 0.26941967 0.0002474155 2 1 0
+114 2 2 0.0015654293382317786 0.9984358 0.0015639706 3.5057428E-07 2 1 0
+116 2 2 0.082808513856507754 0.9205274 0.07945967 1.3067251E-05 2 1 0
+118 2 2 8.392686160427524E-05 0.9999161 8.372341E-05 1.07175394E-10 2 1 0
+119 2 2 0.085332889130497608 0.9182066 0.081782915 1.0257855E-05 2 1 0
+124 2 2 0.03029776689626696 0.9701566 0.02983821 5.02674E-06 2 1 0
+126 2 2 0.42805961427942696 0.65177256 0.34790462 0.00032288703 2 1 0
+127 2 2 0.37273980651049665 0.68884444 0.31086212 0.0002934189 2 1 0
+130 2 2 0.023508540435441566 0.97676563 0.023233732 5.989922E-07 2 1 0
+134 2 2 0.039031011947558578 0.9617209 0.038277302 1.6219885E-06 2 1 0
+135 2 2 0.01278401224870694 0.98729736 0.012701993 5.567945E-07 2 1 0
+136 2 2 0.0040655412946085491 0.9959427 0.0040563564 8.3937186E-07 2 1 0
+139 2 2 0.12014629655188541 0.8867907 0.11317021 3.931525E-05 2 1 0
+140 2 2 0.0054330927887431615 0.99458164 0.0054175323 7.817332E-07 2 1 0
+142 2 2 0.016407972136600949 0.9837259 0.016270882 3.321601E-06 2 1 0
+143 2 2 0.0040641049565237689 0.99594414 0.00405544 2.884136E-07 2 1 0
+146 2 2 0.063336850854471993 0.93862724 0.061356686 1.6151482E-05 2 1 0
+148 2 2 0.013229351377581255 0.9868578 0.013137038 5.0962526E-06 2 1 0
+149 2 2 0.090927626330238626 0.9130838 0.086879514 3.6539095E-05 2 1 0
diff --git a/test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-TrainTest-iris.txt b/test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-TrainTest-iris.txt
new file mode 100644
index 0000000000..1f136318b1
--- /dev/null
+++ b/test/BaselineOutput/SingleDebug/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-TrainTest-iris.txt
@@ -0,0 +1,151 @@
+Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class
+0 0 0 0.0011148704666220459 0.99888575 0.0011145204 1.3776857E-13 0 1 2
+1 0 0 0.0070600595001449043 0.9929648 0.007035469 5.980848E-12 0 1 2
+2 0 0 0.0023107383603406979 0.9976919 0.002307759 1.30608E-12 0 1 2
+3 0 0 0.0071973505498515739 0.9928285 0.0071717617 1.5530932E-11 0 1 2
+4 0 0 0.00077247645619236299 0.9992278 0.0007718982 9.492881E-14 0 1 2
+5 0 0 0.00070763824108637647 0.9992926 0.00070762733 1.7460614E-13 0 1 2
+6 0 0 0.0015249548965825181 0.9984762 0.0015239433 1.9716312E-12 0 1 2
+7 0 0 0.0023698852189009997 0.9976329 0.0023674963 7.53184E-13 0 1 2
+8 0 0 0.01027010486628236 0.98978245 0.010217615 4.4595366E-11 0 1 2
+9 0 0 0.0076313190029468729 0.9923977 0.0076022604 3.5732832E-12 0 1 2
+10 0 0 0.00077915735755335308 0.99922115 0.0007790165 3.6408105E-14 0 1 2
+11 0 0 0.0034885388576309144 0.99651754 0.0034823413 2.83667E-12 0 1 2
+12 0 0 0.0075292801506795124 0.992499 0.0075008543 4.1048614E-12 0 1 2
+13 0 0 0.0024023278603287428 0.99760056 0.0023991016 1.5549362E-12 0 1 2
+14 0 0 8.1065602621708377E-05 0.99991894 8.0906575E-05 1.6902284E-16 0 1 2
+15 0 0 5.149973917474053E-05 0.9999485 5.1647687E-05 6.3881086E-16 0 1 2
+16 0 0 0.00015259953334155612 0.9998474 0.00015233555 7.692787E-15 0 1 2
+17 0 0 0.0010471460236901183 0.9989534 0.0010464673 2.6370047E-13 0 1 2
+18 0 0 0.0010852740224894391 0.9989153 0.0010850534 7.665227E-14 0 1 2
+19 0 0 0.00050831837325152984 0.9994918 0.00050798786 8.2925454E-14 0 1 2
+20 0 0 0.0050735213310076762 0.9949393 0.005060825 1.1999263E-12 0 1 2
+21 0 0 0.00068950576665922968 0.99931073 0.00068974565 3.0276918E-13 0 1 2
+22 0 0 0.00016690693355841423 0.9998331 0.00016714552 1.24774365E-14 0 1 2
+23 0 0 0.0060968509418567766 0.9939217 0.006078474 3.6398856E-11 0 1 2
+24 0 0 0.011000658927949667 0.9890596 0.010940627 2.9289283E-11 0 1 2
+25 0 0 0.015137646970205936 0.98497635 0.015023758 2.1512653E-11 0 1 2
+26 0 0 0.0030670062937996402 0.9969377 0.003062062 6.018899E-12 0 1 2
+27 0 0 0.0016346217426049894 0.9983667 0.0016332197 2.287227E-13 0 1 2
+28 0 0 0.0016097859543283302 0.9983915 0.0016089765 1.9991067E-13 0 1 2
+29 0 0 0.0072956329818799339 0.9927309 0.007268736 1.3519734E-11 0 1 2
+30 0 0 0.010519988636096064 0.98953515 0.010464923 1.956453E-11 0 1 2
+31 0 0 0.0020780682572110079 0.9979241 0.0020754703 9.251467E-13 0 1 2
+32 0 0 0.0001897991996934539 0.9998102 0.00019020819 2.480733E-15 0 1 2
+33 0 0 8.392686160427524E-05 0.9999161 8.372972E-05 5.0225263E-16 0 1 2
+34 0 0 0.0076313190029468729 0.9923977 0.0076022604 3.5732832E-12 0 1 2
+35 0 0 0.0015668620918511083 0.99843436 0.0015660885 2.637719E-13 0 1 2
+36 0 0 0.00075529720050108832 0.999245 0.00075494894 2.1162744E-14 0 1 2
+37 0 0 0.0076313190029468729 0.9923977 0.0076022604 3.5732832E-12 0 1 2
+38 0 0 0.0048484729816518539 0.99516326 0.0048371274 1.0765147E-11 0 1 2
+39 0 0 0.0023670174117273336 0.9976358 0.0023640736 5.7310146E-13 0 1 2
+40 0 0 0.00071431870928224299 0.99928594 0.0007139834 1.5880794E-13 0 1 2
+41 0 0 0.058616606694306482 0.94306827 0.05693195 1.3674992E-09 0 1 2
+42 0 0 0.0023202972115890941 0.9976824 0.002317787 2.964662E-12 0 1 2
+43 0 0 0.0018701744864162277 0.9981316 0.0018682877 1.1569887E-11 0 1 2
+44 0 0 0.0022153938202876546 0.99778706 0.0022129146 3.5982877E-12 0 1 2
+45 0 0 0.0066394791182328066 0.9933825 0.0066177957 1.5050398E-11 0 1 2
+46 0 0 0.00079442815684458367 0.9992059 0.0007942036 9.45553E-14 0 1 2
+47 0 0 0.0033950556005307988 0.9966107 0.0033896863 3.7430263E-12 0 1 2
+48 0 0 0.00078011177567713298 0.9992202 0.00078014436 4.7848332E-14 0 1 2
+49 0 0 0.0023355318196242821 0.9976672 0.0023323793 6.583122E-13 0 1 2
+50 1 1 0.0060477374096205563 0.9939705 0.0040119863 0.002017312 1 2 0
+51 1 1 0.023358802522272765 0.9769119 0.018574875 0.0045133037 1 2 0
+52 1 1 0.03167006942034075 0.9688262 0.030503416 0.0006706227 1 2 0
+53 1 1 0.085153806889826739 0.918371 0.080720015 0.0009088988 1 2 0
+54 1 1 0.063849951086753426 0.93814576 0.061179116 0.0006753106 1 2 0
+55 1 1 0.089629429284646664 0.9142699 0.08488794 0.00084198435 1 2 0
+56 1 1 0.08483648364691608 0.9186625 0.07830901 0.0030285995 1 2 0
+57 1 1 0.019588462132268222 0.98060215 0.016944302 0.0024533693 1 0 2
+58 1 1 0.0099349166147827024 0.9901143 0.008975584 0.0009100606 1 2 0
+59 1 1 0.092321899949188477 0.9118116 0.08203742 0.006150943 1 2 0
+60 1 1 0.014485823445928967 0.9856186 0.012574332 0.0018072706 1 2 0
+61 1 1 0.045088304308168195 0.95591307 0.037453618 0.006633371 1 2 0
+62 1 1 0.0040779297962239254 0.9959304 0.00350171 0.00056805444 1 2 0
+63 1 1 0.10219954324186892 0.9028494 0.09655303 0.0005976357 1 2 0
+64 1 1 0.04381778370401148 0.95712835 0.040380865 0.0024907354 1 0 2
+65 1 1 0.0080509348386470451 0.9919814 0.004386741 0.0036320223 1 0 2
+66 1 1 0.25712513878794285 0.77327144 0.22504078 0.0016878267 1 2 0
+67 1 1 0.0046958738522607582 0.99531513 0.0024410768 0.002243941 1 0 2
+68 1 1 0.42020607515071323 0.65691143 0.3430131 7.5479635E-05 1 2 0
+69 1 1 0.0088743981357345281 0.99116486 0.006175138 0.002660179 1 2 0
+70 1 2 1.119156119237483 0.67287296 0.32655525 0.00057173334 2 1 0
+71 1 1 0.010574322077426965 0.9894814 0.006254719 0.0042638187 1 0 2
+72 1 1 0.61366271154662444 0.5413644 0.45859504 4.0561474E-05 1 2 0
+73 1 1 0.03377105479336788 0.9667928 0.03281732 0.0003900002 1 2 0
+74 1 1 0.0076222497957970196 0.9924067 0.004714336 0.0028790454 1 2 0
+75 1 1 0.0093469378209280057 0.9906966 0.006279014 0.0030244375 1 2 0
+76 1 1 0.03111313795737956 0.9693659 0.030329114 0.00030520634 1 2 0
+77 1 1 0.36622901478232017 0.693344 0.30640054 0.00025561816 1 2 0
+78 1 1 0.12305449703206547 0.8842155 0.11444254 0.0013420355 1 2 0
+79 1 1 0.017178073477986378 0.9829686 0.016676014 0.00035517136 1 0 2
+80 1 1 0.0099248633048492161 0.9901242 0.007182131 0.002693587 1 2 0
+81 1 1 0.0061249768712999088 0.99389374 0.0037273536 0.0023786854 1 0 2
+82 1 1 0.01018477684487269 0.9898669 0.0059374967 0.0041955533 1 0 2
+83 1 2 1.7848181575763675 0.8321596 0.16782758 1.2941472E-05 2 1 0
+84 1 1 0.40771368629306376 0.6651693 0.33338308 0.0014476714 1 2 0
+85 1 1 0.073538094082338953 0.92910075 0.061388977 0.009510201 1 2 0
+86 1 1 0.02568532133252191 0.97464174 0.023907999 0.0014502493 1 2 0
+87 1 1 0.047872087095895285 0.9532557 0.046538867 0.00020534953 1 2 0
+88 1 1 0.023007854444924543 0.9772548 0.014006073 0.008739044 1 2 0
+89 1 1 0.051250957163473519 0.9500402 0.047992557 0.0019672096 1 2 0
+90 1 1 0.088376539509711596 0.9154161 0.08403013 0.0005538561 1 2 0
+91 1 1 0.054428086534615831 0.9470266 0.051642187 0.0013313727 1 2 0
+92 1 1 0.011075027413584841 0.9889861 0.008220732 0.002793185 1 2 0
+93 1 1 0.01437425449213319 0.98572856 0.011792642 0.002478861 1 0 2
+94 1 1 0.049662849884455844 0.9515502 0.046534896 0.0019149618 1 2 0
+95 1 1 0.013598453800202021 0.9864936 0.00785744 0.0056490703 1 2 0
+96 1 1 0.025311486855273613 0.97500616 0.020882485 0.004111272 1 2 0
+97 1 1 0.01101301311502036 0.9890474 0.00809153 0.002861004 1 2 0
+98 1 1 0.081760169588047965 0.92149293 0.0778774 0.00062952057 1 0 2
+99 1 1 0.023015234494193421 0.9772476 0.018569632 0.0041829064 1 2 0
+100 2 2 0.00011158612051911557 0.9998884 0.00011122978 4.3882338E-09 2 1 0
+101 2 2 0.013729575340933843 0.98636425 0.0136345485 1.266714E-06 2 1 0
+102 2 2 0.010888574253525379 0.9891705 0.010829585 1.6294389E-07 2 1 0
+103 2 2 0.025930890575031128 0.9744024 0.025596872 6.894996E-07 2 1 0
+104 2 2 0.0015621459522632778 0.9984391 0.0015607552 3.6407382E-08 2 1 0
+105 2 2 0.0026502530714843367 0.99735326 0.0026466271 2.7263904E-09 2 1 0
+106 2 2 0.030433677359253137 0.97002476 0.0299635 1.159951E-05 2 1 0
+107 2 2 0.024480795647284091 0.9758164 0.024183862 4.4924544E-08 2 1 0
+108 2 2 0.011575682088038546 0.98849106 0.01150899 3.3041104E-08 2 1 0
+109 2 2 0.0019807750421713521 0.9980212 0.0019784786 1.6299222E-07 2 1 0
+110 2 2 0.1668877744415341 0.8462946 0.15360819 9.722446E-05 2 1 0
+111 2 2 0.031444061158942889 0.96904516 0.030953335 1.3455377E-06 2 1 0
+112 2 2 0.023395533219334613 0.976876 0.023122288 1.6099556E-06 2 1 0
+113 2 2 0.004405951750777239 0.99560374 0.0043958076 3.048161E-07 2 1 0
+114 2 2 0.00051786001087093976 0.9994823 0.00051763566 9.5351375E-08 2 1 0
+115 2 2 0.0073614401801424031 0.9926656 0.0073315674 2.597062E-06 2 1 0
+116 2 2 0.085010512269807784 0.9185026 0.08149283 4.67563E-06 2 1 0
+117 2 2 0.010475174804130676 0.9895795 0.0104205115 1.4943078E-07 2 1 0
+118 2 2 8.392686160427524E-05 0.9999161 8.4219195E-05 7.1131295E-12 2 1 0
+119 2 2 0.14243270625738888 0.8672459 0.13275184 2.228693E-06 2 1 0
+120 2 2 0.0058665359402536059 0.99415064 0.0058489824 4.4902038E-07 2 1 0
+121 2 2 0.011418315077759932 0.9886466 0.011349821 3.4921036E-06 2 1 0
+122 2 2 0.0027408576136189505 0.9972629 0.0027368125 8.6288726E-10 2 1 0
+123 2 2 0.21712063090760428 0.8048329 0.19513033 3.696039E-05 2 1 0
+124 2 2 0.018555186660661513 0.9816159 0.018382292 1.7941145E-06 2 1 0
+125 2 2 0.13317062821825568 0.8753157 0.12468223 2.215571E-06 2 1 0
+126 2 2 0.30929185172157869 0.7339665 0.26592687 0.00010682235 2 1 0
+127 2 2 0.28011468210882728 0.7556971 0.24416323 0.00013955706 2 1 0
+128 2 2 0.0030803390471882929 0.9969244 0.0030754893 6.93042E-08 2 1 0
+129 2 2 0.5607895853823287 0.5707582 0.42923495 6.927903E-06 2 1 0
+130 2 2 0.026384817227264001 0.9739602 0.026039718 7.690115E-08 2 1 0
+131 2 2 0.22129675374786231 0.8014788 0.19851315 7.966131E-06 2 1 0
+132 2 2 0.0015125382870987553 0.9984886 0.0015110222 3.6266687E-08 2 1 0
+133 2 1 0.80010965478119345 0.55066234 0.4492797 5.7917074E-05 1 2 0
+134 2 2 0.10865733918867169 0.89703774 0.10296163 7.100352E-07 2 1 0
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+137 2 2 0.085431109138005057 0.9181164 0.08187672 6.785138E-06 2 1 0
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+141 2 2 0.047074804913225708 0.95401603 0.0459597 2.4441237E-05 2 1 0
+142 2 2 0.013729575340933843 0.98636425 0.0136345485 1.266714E-06 2 1 0
+143 2 2 0.0020265834982710466 0.99797547 0.0020240443 7.197046E-08 2 1 0
+144 2 2 0.0010843193130689751 0.99891627 0.0010831588 1.3605347E-07 2 1 0
+145 2 2 0.014166328018634792 0.98593354 0.0140630165 3.5111582E-06 2 1 0
+146 2 2 0.04495238264372909 0.956043 0.04395422 2.8865643E-06 2 1 0
+147 2 2 0.067826276148074821 0.9344228 0.065563805 1.3508331E-05 2 1 0
+148 2 2 0.005008703171980048 0.9950038 0.0049937237 2.5133447E-06 2 1 0
+149 2 2 0.081482913321255249 0.92174846 0.07823061 2.0680885E-05 2 1 0
diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt
new file mode 100644
index 0000000000..c24820c51d
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt
@@ -0,0 +1,58 @@
+maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% 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 (over 1 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 8 instances with missing features during training (over 1 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 || 132 | 2 | 0.9851
+ negative || 9 | 211 | 0.9591
+ ||======================
+Precision || 0.9362 | 0.9906 |
+OVERALL 0/1 ACCURACY: 0.968927
+LOG LOSS/instance: Infinity
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): -Infinity
+AUC: 0.992809
+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 || 102 | 3 | 0.9714
+ negative || 4 | 220 | 0.9821
+ ||======================
+Precision || 0.9623 | 0.9865 |
+OVERALL 0/1 ACCURACY: 0.978723
+LOG LOSS/instance: 0.239330
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.735095
+AUC: 0.997279
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.995044 (0.0022)
+Accuracy: 0.973825 (0.0049)
+Positive precision: 0.949217 (0.0130)
+Positive recall: 0.978252 (0.0068)
+Negative precision: 0.988579 (0.0020)
+Negative recall: 0.970617 (0.0115)
+Log-loss: Infinity (NaN)
+Log-loss reduction: -Infinity (NaN)
+F1 Score: 0.963412 (0.0034)
+AUPRC: 0.990159 (0.0038)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt
new file mode 100644
index 0000000000..aaf6943c00
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt
@@ -0,0 +1,4 @@
+AveragedPerceptron
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.995044 0.973825 0.949217 0.978252 0.988579 0.970617 Infinity -Infinity 0.963412 0.990159 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=%Output% data=%Data% seed=1
+
diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt
new file mode 100644
index 0000000000..401503d4e9
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
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+684 0 -2.9462147 1E-15 1.4415419267167138E-15 0
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diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt
new file mode 100644
index 0000000000..b683beedd9
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt
@@ -0,0 +1,56 @@
+maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=%Output% data=%Data% 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 (over 1 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 8 instances with missing features during training (over 1 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 || 9 | 211 | 0.9591
+ ||======================
+Precision || 0.9362 | 0.9906 |
+OVERALL 0/1 ACCURACY: 0.968927
+LOG LOSS/instance: 0.155883
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): 0.837113
+AUC: 0.992809
+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 || 102 | 3 | 0.9714
+ negative || 4 | 220 | 0.9821
+ ||======================
+Precision || 0.9623 | 0.9865 |
+OVERALL 0/1 ACCURACY: 0.978723
+LOG LOSS/instance: 0.130063
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.856038
+AUC: 0.997279
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.995044 (0.0022)
+Accuracy: 0.973825 (0.0049)
+Positive precision: 0.949217 (0.0130)
+Positive recall: 0.978252 (0.0068)
+Negative precision: 0.988579 (0.0020)
+Negative recall: 0.970617 (0.0115)
+Log-loss: 0.142973 (0.0129)
+Log-loss reduction: 0.846576 (0.0095)
+F1 Score: 0.963412 (0.0034)
+AUPRC: 0.990159 (0.0038)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt
new file mode 100644
index 0000000000..2b08b6ae55
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt
@@ -0,0 +1,4 @@
+AveragedPerceptron
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.995044 0.973825 0.949217 0.978252 0.988579 0.970617 0.142973 0.846576 0.963412 0.990159 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=%Output% data=%Data% seed=1
+
diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt
new file mode 100644
index 0000000000..3657243035
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
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+40 0 ? ? ? 0
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diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt
new file mode 100644
index 0000000000..11352e4215
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt
@@ -0,0 +1,58 @@
+maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=%Output% data=%Data% 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 (over 1 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 8 instances with missing features during training (over 1 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 || 132 | 2 | 0.9851
+ negative || 9 | 211 | 0.9591
+ ||======================
+Precision || 0.9362 | 0.9906 |
+OVERALL 0/1 ACCURACY: 0.968927
+LOG LOSS/instance: NaN
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): 0.000000
+AUC: 0.992809
+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 || 102 | 3 | 0.9714
+ negative || 4 | 220 | 0.9821
+ ||======================
+Precision || 0.9623 | 0.9865 |
+OVERALL 0/1 ACCURACY: 0.978723
+LOG LOSS/instance: NaN
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.000000
+AUC: 0.997279
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.995044 (0.0022)
+Accuracy: 0.973825 (0.0049)
+Positive precision: 0.949217 (0.0130)
+Positive recall: 0.978252 (0.0068)
+Negative precision: 0.988579 (0.0020)
+Negative recall: 0.970617 (0.0115)
+Log-loss: NaN (NaN)
+Log-loss reduction: 0.000000 (0.0000)
+F1 Score: 0.963412 (0.0034)
+AUPRC: 0.990159 (0.0038)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt
new file mode 100644
index 0000000000..c8b40ed142
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt
@@ -0,0 +1,4 @@
+AveragedPerceptron
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.995044 0.973825 0.949217 0.978252 0.988579 0.970617 NaN 0 0.963412 0.990159 AveragedPerceptron %Data% %Output% 99 0 0 maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=%Output% data=%Data% seed=1
+
diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration.txt
new file mode 100644
index 0000000000..fda1ad7cbf
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-CV-breast-cancer.nocalibration.txt
@@ -0,0 +1,700 @@
+Instance Label Score Assigned
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diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt
new file mode 100644
index 0000000000..4e70ed0ceb
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt
new file mode 100644
index 0000000000..257962463f
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt
new file mode 100644
index 0000000000..a39a23abdd
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/AveragedPerceptron/netcoreapp31/AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-out.txt
new file mode 100644
index 0000000000..6d166f9706
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-out.txt
@@ -0,0 +1,121 @@
+maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} threads=- norm=No dout=%Output% data=%Data% 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: 0.111875
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.876170
+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: 0.126419 (0.0145)
+Log-loss reduction: 0.864436 (0.0117)
+F1 Score: 0.946603 (0.0054)
+AUPRC: 0.991761 (0.0020)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'LBFGS data prep' started.
+[1] 'LBFGS data prep' finished in %Time%.
+[2] 'LBFGS Optimizer' started.
+[2] (%Time%) 0 iterations Loss: 0.6931471824645996
+[2] (%Time%) 1 iterations Loss: 0.6544521450996399 Improvement: 0.0387
+[2] (%Time%) 2 iterations Loss: 0.6425679922103882 Improvement: 0.01725
+[2] (%Time%) 3 iterations Loss: 0.6358204483985901 Improvement: 0.009247
+[2] (%Time%) 4 iterations Loss: 0.6251664161682129 Improvement: 0.01031
+[2] (%Time%) 5 iterations Loss: 0.5822217464447021 Improvement: 0.03481
+[2] (%Time%) 6 iterations Loss: 0.2409491091966629 Improvement: 0.2647
+[2] (%Time%) 7 iterations Loss: 0.18065251410007477 Improvement: 0.1114
+[2] (%Time%) 8 iterations Loss: 0.12022750824689865 Improvement: 0.07317
+[2] (%Time%) 9 iterations Loss: 0.09626178443431854 Improvement: 0.03627
+[2] (%Time%) 10 iterations Loss: 0.08480900526046753 Improvement: 0.01766
+[2] (%Time%) 11 iterations Loss: 0.07748782634735107 Improvement: 0.009905
+[2] (%Time%) 12 iterations Loss: 0.07508619874715805 Improvement: 0.004277
+[2] (%Time%) 13 iterations Loss: 0.07309562712907791 Improvement: 0.002562
+[2] (%Time%) 14 iterations Loss: 0.07258497178554535 Improvement: 0.001024
+[2] (%Time%) 15 iterations Loss: 0.07184841483831406 Improvement: 0.0008083
+[2] (%Time%) 16 iterations Loss: 0.07156649976968765 Improvement: 0.0004135
+[2] (%Time%) 17 iterations Loss: 0.0715627521276474 Improvement: 0.0001062
+[2] (%Time%) 18 iterations Loss: 0.07146956771612167 Improvement: 9.644E-05
+[2] 'LBFGS Optimizer' finished in %Time%.
+[3] 'LBFGS data prep #2' started.
+[3] 'LBFGS data prep #2' finished in %Time%.
+[4] 'LBFGS Optimizer #2' started.
+[4] (%Time%) 0 iterations Loss: 0.6931471824645996
+[4] (%Time%) 1 iterations Loss: 0.6328520774841309 Improvement: 0.0603
+[4] (%Time%) 2 iterations Loss: 0.620979368686676 Improvement: 0.02156
+[4] (%Time%) 3 iterations Loss: 0.613497793674469 Improvement: 0.01083
+[4] (%Time%) 4 iterations Loss: 0.6002617478370667 Improvement: 0.01264
+[4] (%Time%) 5 iterations Loss: 0.5936987996101379 Improvement: 0.008078
+[4] (%Time%) 6 iterations Loss: 0.5855146050453186 Improvement: 0.008158
+[4] (%Time%) 7 iterations Loss: 0.557903528213501 Improvement: 0.02275
+[4] (%Time%) 8 iterations Loss: 0.514011561870575 Improvement: 0.03861
+[4] (%Time%) 9 iterations Loss: 0.47046783566474915 Improvement: 0.04231
+[4] (%Time%) 10 iterations Loss: 0.3519411087036133 Improvement: 0.09947
+[4] (%Time%) 11 iterations Loss: 0.24160850048065186 Improvement: 0.1076
+[4] (%Time%) 12 iterations Loss: 0.15622486174106598 Improvement: 0.09094
+[4] (%Time%) 13 iterations Loss: 0.13706204295158386 Improvement: 0.03711
+[4] (%Time%) 14 iterations Loss: 0.12257859110832214 Improvement: 0.02014
+[4] (%Time%) 15 iterations Loss: 0.112119160592556 Improvement: 0.01288
+[4] (%Time%) 16 iterations Loss: 0.10575493425130844 Improvement: 0.007993
+[4] (%Time%) 17 iterations Loss: 0.10021665692329407 Improvement: 0.006152
+[4] (%Time%) 18 iterations Loss: 0.09684965759515762 Improvement: 0.004063
+[4] (%Time%) 19 iterations Loss: 0.09285992383956909 Improvement: 0.004008
+[4] (%Time%) 20 iterations Loss: 0.09131418168544769 Improvement: 0.002161
+[4] (%Time%) 21 iterations Loss: 0.09053913503885269 Improvement: 0.001122
+[4] (%Time%) 22 iterations Loss: 0.09002908319234848 Improvement: 0.0006629
+[4] (%Time%) 23 iterations Loss: 0.08863696455955505 Improvement: 0.00121
+[4] (%Time%) 24 iterations Loss: 0.08813063055276871 Improvement: 0.0006822
+[4] (%Time%) 25 iterations Loss: 0.0875096321105957 Improvement: 0.0006363
+[4] (%Time%) 26 iterations Loss: 0.08728791028261185 Improvement: 0.0003254
+[4] (%Time%) 27 iterations Loss: 0.08721961826086044 Improvement: 0.0001326
+[4] (%Time%) 28 iterations Loss: 0.08721006661653519 Improvement: 4.03E-05
+[4] 'LBFGS Optimizer #2' finished in %Time%.
diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-rp.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-rp.txt
new file mode 100644
index 0000000000..5a076145d7
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+LogisticRegression
+AUC Accuracy Positive precision Positive recall Negative precision Negative recall Log-loss Log-loss reduction F1 Score AUPRC /l2 /ot /nt /nn Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.995752 0.963401 0.955651 0.938486 0.968914 0.975122 0.126419 0.864436 0.946603 0.991761 0.1 0.0001 1 + LogisticRegression %Data% %Output% 99 0 0 maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} threads=- norm=No dout=%Output% data=%Data% seed=1 /l2:0.1;/ot:0.0001;/nt:1;/nn:+
+
diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer.txt
new file mode 100644
index 0000000000..26fdb16999
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-CV-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
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+40 0 ? ? ? 0
+41 1 2.7845955 0.94183767 0.086449671087919164 1
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diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..af2726b69b
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,71 @@
+maml.exe TrainTest test=%Data% tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} norm=No dout=%Output% data=%Data% out=%Output% 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): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'LBFGS data prep' started.
+[1] 'LBFGS data prep' finished in %Time%.
+[2] 'LBFGS Optimizer' started.
+[2] (%Time%) 0 iterations Loss: 0.6931471824645996
+[2] (%Time%) 1 iterations Loss: 0.6448327898979187 Improvement: 0.04831
+[2] (%Time%) 2 iterations Loss: 0.6306551098823547 Improvement: 0.02101
+[2] (%Time%) 3 iterations Loss: 0.6191107034683228 Improvement: 0.0138
+[2] (%Time%) 4 iterations Loss: 0.6139997243881226 Improvement: 0.007257
+[2] (%Time%) 5 iterations Loss: 0.6017452478408813 Improvement: 0.01101
+[2] (%Time%) 6 iterations Loss: 0.5552629232406616 Improvement: 0.03762
+[2] (%Time%) 7 iterations Loss: 0.40808025002479553 Improvement: 0.1198
+[2] (%Time%) 8 iterations Loss: 0.22476567327976227 Improvement: 0.1674
+[2] (%Time%) 9 iterations Loss: 0.17795756459236145 Improvement: 0.07696
+[2] (%Time%) 10 iterations Loss: 0.13737377524375916 Improvement: 0.04968
+[2] (%Time%) 11 iterations Loss: 0.11691863089799881 Improvement: 0.02776
+[2] (%Time%) 12 iterations Loss: 0.10186515003442764 Improvement: 0.01823
+[2] (%Time%) 13 iterations Loss: 0.09181062132120132 Improvement: 0.0121
+[2] (%Time%) 14 iterations Loss: 0.08791223913431168 Improvement: 0.005948
+[2] (%Time%) 15 iterations Loss: 0.08431486040353775 Improvement: 0.004185
+[2] (%Time%) 16 iterations Loss: 0.08275849372148514 Improvement: 0.002214
+[2] (%Time%) 17 iterations Loss: 0.08134783804416656 Improvement: 0.001611
+[2] (%Time%) 18 iterations Loss: 0.08036927133798599 Improvement: 0.001137
+[2] (%Time%) 19 iterations Loss: 0.07970956712961197 Improvement: 0.000779
+[2] (%Time%) 20 iterations Loss: 0.07952727377414703 Improvement: 0.0003315
+[2] (%Time%) 21 iterations Loss: 0.07951106876134872 Improvement: 9.502E-05
+[2] 'LBFGS Optimizer' finished in %Time%.
+[3] 'Saving model' started.
+[3] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer.txt b/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..5e638a505e
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/LogisticRegression/netcoreapp31/LogisticRegression-non-negative-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance Label Score Probability Log-loss Assigned
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+684 0 -7.007458 0.0009042878 0.0013052017434545531 0
+685 0 -7.007458 0.0009042878 0.0013052017434545531 0
+686 0 -7.007458 0.0009042878 0.0013052017434545531 0
+687 0 -5.05263 0.0063518947 0.0091930747114589567 0
+688 0 -5.397394 0.004507952 0.0065183030183428321 0
+689 0 -4.404107 0.012079325 0.017532888869781153 0
+690 0 -6.371273 0.0017070619 0.002464874172082224 0
+691 1 2.841361 0.9448704 0.081811615585610037 1
+692 0 -5.934082 0.0026406606 0.0038147068729553536 0
+693 0 -5.130745 0.0058774063 0.0085043212036809823 0
+694 0 -5.475578 0.0041703433 0.0060291140626305435 0
+695 0 -6.4707704 0.00154564 0.0022316123258321369 0
+696 1 5.1025305 0.99395543 0.0087469287128904821 1
+697 1 3.3554373 0.9662824 0.049483169875034191 1
+698 1 4.277025 0.98630625 0.019892418981030672 1
diff --git a/test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris-out.txt b/test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris-out.txt
new file mode 100644
index 0000000000..6f8b64ff3c
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris-out.txt
@@ -0,0 +1,111 @@
+maml.exe CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=%Output% data=%Data% seed=1 xf=Term{col=Label}
+Not adding a normalizer.
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 15 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 15 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: 0.101866
+Log-loss reduction: 0.906331
+
+Confusion table
+ ||========================
+PREDICTED || 0 | 1 | 2 | Recall
+TRUTH ||========================
+ 0 || 29 | 0 | 0 | 1.0000
+ 1 || 0 | 19 | 1 | 0.9500
+ 2 || 0 | 0 | 22 | 1.0000
+ ||========================
+Precision ||1.0000 |1.0000 |0.9565 |
+Accuracy(micro-avg): 0.985915
+Accuracy(macro-avg): 0.983333
+Log-loss: 0.075796
+Log-loss reduction: 0.930183
+
+OVERALL RESULTS
+---------------------------------------
+Accuracy(micro-avg): 0.973970 (0.0119)
+Accuracy(macro-avg): 0.974206 (0.0091)
+Log-loss: 0.088831 (0.0130)
+Log-loss reduction: 0.918257 (0.0119)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'Building term dictionary' started.
+[1] (%Time%) 71 examples Total Terms: 3
+[1] 'Building term dictionary' finished in %Time%.
+[2] 'LBFGS data prep' started.
+[2] 'LBFGS data prep' finished in %Time%.
+[3] 'LBFGS Optimizer' started.
+[3] (%Time%) 0 iterations Loss: 1.0986123085021973
+[3] (%Time%) 1 iterations Loss: 0.9755012392997742 Improvement: 0.1231
+[3] (%Time%) 2 iterations Loss: 0.8284684419631958 Improvement: 0.1422
+[3] (%Time%) 3 iterations Loss: 0.49238577485084534 Improvement: 0.2899
+[3] (%Time%) 4 iterations Loss: 0.4102630317211151 Improvement: 0.1335
+[3] (%Time%) 5 iterations Loss: 0.3732020854949951 Improvement: 0.06109
+[3] (%Time%) 6 iterations Loss: 0.3262292444705963 Improvement: 0.0505
+[3] (%Time%) 7 iterations Loss: 0.30860307812690735 Improvement: 0.02584
+[3] (%Time%) 8 iterations Loss: 0.24991190433502197 Improvement: 0.05048
+[3] (%Time%) 9 iterations Loss: 0.19703027606010437 Improvement: 0.05228
+[3] (%Time%) 10 iterations Loss: 0.1837688386440277 Improvement: 0.02302
+[3] (%Time%) 11 iterations Loss: 0.17426884174346924 Improvement: 0.01288
+[3] (%Time%) 12 iterations Loss: 0.14890950918197632 Improvement: 0.02224
+[3] (%Time%) 13 iterations Loss: 0.14668036997318268 Improvement: 0.007232
+[3] (%Time%) 14 iterations Loss: 0.12762901186943054 Improvement: 0.0161
+[3] (%Time%) 15 iterations Loss: 0.12740232050418854 Improvement: 0.004194
+[3] (%Time%) 16 iterations Loss: 0.12709592282772064 Improvement: 0.001278
+[3] (%Time%) 17 iterations Loss: 0.12680405378341675 Improvement: 0.0005385
+[3] 'LBFGS Optimizer' finished in %Time%.
+[4] 'Building term dictionary #2' started.
+[4] (%Time%) 79 examples Total Terms: 3
+[4] 'Building term dictionary #2' finished in %Time%.
+[5] 'LBFGS data prep #2' started.
+[5] 'LBFGS data prep #2' finished in %Time%.
+[6] 'LBFGS Optimizer #2' started.
+[6] (%Time%) 0 iterations Loss: 1.0986123085021973
+[6] (%Time%) 1 iterations Loss: 1.036556363105774 Improvement: 0.06206
+[6] (%Time%) 2 iterations Loss: 1.0036184787750244 Improvement: 0.03876
+[6] (%Time%) 3 iterations Loss: 0.9370792508125305 Improvement: 0.05993
+[6] (%Time%) 4 iterations Loss: 0.8192444443702698 Improvement: 0.1035
+[6] (%Time%) 5 iterations Loss: 0.728320837020874 Improvement: 0.09407
+[6] (%Time%) 6 iterations Loss: 0.5819926261901855 Improvement: 0.1333
+[6] (%Time%) 7 iterations Loss: 0.4406244158744812 Improvement: 0.1393
+[6] (%Time%) 8 iterations Loss: 0.36817970871925354 Improvement: 0.08917
+[6] (%Time%) 9 iterations Loss: 0.28754767775535583 Improvement: 0.08277
+[6] (%Time%) 10 iterations Loss: 0.23988358676433563 Improvement: 0.05644
+[6] (%Time%) 11 iterations Loss: 0.21770016849040985 Improvement: 0.03075
+[6] (%Time%) 12 iterations Loss: 0.20622937381267548 Improvement: 0.01629
+[6] (%Time%) 13 iterations Loss: 0.1928335726261139 Improvement: 0.01412
+[6] (%Time%) 14 iterations Loss: 0.18503741919994354 Improvement: 0.009377
+[6] (%Time%) 15 iterations Loss: 0.1817353218793869 Improvement: 0.004821
+[6] (%Time%) 16 iterations Loss: 0.16841556131839752 Improvement: 0.0112
+[6] (%Time%) 17 iterations Loss: 0.1592421531677246 Improvement: 0.009679
+[6] (%Time%) 18 iterations Loss: 0.15063226222991943 Improvement: 0.008877
+[6] (%Time%) 19 iterations Loss: 0.14185991883277893 Improvement: 0.008799
+[6] (%Time%) 20 iterations Loss: 0.1356329470872879 Improvement: 0.00687
+[6] (%Time%) 21 iterations Loss: 0.13491712510585785 Improvement: 0.002254
+[6] (%Time%) 22 iterations Loss: 0.1332564502954483 Improvement: 0.001809
+[6] (%Time%) 23 iterations Loss: 0.13261565566062927 Improvement: 0.0009329
+[6] 'LBFGS Optimizer #2' finished in %Time%.
diff --git a/test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris.txt b/test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris.txt
new file mode 100644
index 0000000000..a077f1e9b6
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-CV-iris.txt
@@ -0,0 +1,151 @@
+Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class
+5 0 0 0.0039186265649996796 0.99608904 0.003910809 2.0865299E-11 0 1 2
+6 0 0 0.0066394791182328066 0.9933825 0.006617098 2.152395E-10 0 1 2
+8 0 0 0.025494595703479419 0.97482765 0.025172155 2.838915E-09 0 1 2
+9 0 0 0.019190772567240676 0.9809922 0.01900817 2.7058017E-10 0 1 2
+10 0 0 0.0035562493991592786 0.99645007 0.003550249 4.9272335E-12 0 1 2
+11 0 0 0.012517929979546309 0.9875601 0.0124394335 2.8008076E-10 0 1 2
+18 0 0 0.0047769616990695207 0.99523443 0.004765985 8.127231E-12 0 1 2
+20 0 0 0.014707546996037419 0.9854001 0.014600216 8.914893E-11 0 1 2
+21 0 0 0.003647833406238951 0.9963588 0.003641376 3.5084515E-11 0 1 2
+25 0 0 0.032317619176197167 0.968199 0.03180108 1.10023E-09 0 1 2
+28 0 0 0.0057745087199158104 0.99424213 0.0057580057 2.0235997E-11 0 1 2
+31 0 0 0.0071621105155360203 0.9928635 0.0071367947 6.242954E-11 0 1 2
+32 0 0 0.0014066451993830036 0.99859434 0.0014055507 7.3242096E-13 0 1 2
+35 0 0 0.0052270768592320212 0.99478656 0.0052133226 2.5026661E-11 0 1 2
+37 0 0 0.019190772567240676 0.9809922 0.01900817 2.7058017E-10 0 1 2
+40 0 0 0.0033063652339419308 0.9966991 0.003300911 1.895811E-11 0 1 2
+41 0 0 0.077088846429646946 0.9258076 0.0741926 3.3343277E-08 0 1 2
+44 0 0 0.010642032182541446 0.9894144 0.0105858175 3.5036918E-10 0 1 2
+45 0 0 0.017220520546649732 0.9829269 0.017073102 8.5334684E-10 0 1 2
+46 0 0 0.0041847046934752278 0.99582404 0.0041763177 1.5150974E-11 0 1 2
+48 0 0 0.0036583023932260659 0.9963484 0.0036516767 6.700264E-12 0 1 2
+50 1 1 0.0098977138531054429 0.9901511 0.005578102 0.0042707166 1 2 0
+51 1 1 0.031642446182477788 0.96885294 0.024089899 0.007057201 1 2 0
+52 1 1 0.03573585794885261 0.9648951 0.033440154 0.001664746 1 2 0
+54 1 1 0.072409637391112461 0.9301498 0.06803778 0.0018123286 1 2 0
+56 1 1 0.093649650619783725 0.91060174 0.08499099 0.0044072797 1 2 0
+60 1 1 0.042489542543681612 0.9584005 0.03626706 0.005332458 1 2 0
+63 1 1 0.137502875229945 0.87153184 0.1271067 0.0013614136 1 2 0
+64 1 1 0.054985501307998973 0.9464989 0.048023954 0.0054772357 1 0 2
+66 1 1 0.32485007538570837 0.7226357 0.27483696 0.0025274218 1 2 0
+68 1 1 0.41495870735351531 0.66036755 0.33927307 0.00035943443 1 2 0
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+73 1 1 0.061366106483072287 0.94047886 0.058446974 0.001074166 1 2 0
+74 1 1 0.01440358171033043 0.98569965 0.008161617 0.0061388216 1 2 0
+76 1 1 0.038602842055599461 0.96213275 0.036847353 0.0010197883 1 2 0
+77 1 1 0.29142764060847787 0.7471961 0.25208423 0.00071971776 1 2 0
+79 1 1 0.031789553326302268 0.9687104 0.030109784 0.0011796883 1 0 2
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+100 2 2 0.00061608453557407644 0.9993841 0.0006157393 5.2001777E-08 2 1 0
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+0 0 0 0.0038471219092532103 0.99616027 0.0038398658 4.272304E-13 0 1 2
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+33 0 0 0.00067328225244051433 0.99932694 0.0006728331 2.645334E-15 0 1 2
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+36 0 0 0.0020971218736402456 0.9979051 0.0020952104 4.3312952E-14 0 1 2
+38 0 0 0.012599956415221663 0.9874791 0.012520914 3.2850903E-11 0 1 2
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+47 0 0 0.010122275828759237 0.9899288 0.010071042 1.2646444E-11 0 1 2
+49 0 0 0.0062646587177839679 0.9937549 0.0062455637 1.729813E-12 0 1 2
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+57 1 1 0.034013499575166965 0.96655846 0.030011753 0.0034298701 1 0 2
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+59 1 1 0.089034322664890295 0.9148142 0.07726592 0.007920011 1 2 0
+61 1 1 0.037737148309352063 0.962966 0.028062146 0.008971874 1 2 0
+62 1 1 0.010682094145451054 0.98937476 0.00659236 0.004032912 1 2 0
+65 1 1 0.013174752701392319 0.98691165 0.010195948 0.002892394 1 0 2
+67 1 1 0.011484394147178454 0.9885813 0.007202252 0.0042163264 1 0 2
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+78 1 1 0.11794544915001207 0.88874453 0.108638965 0.0026165938 1 2 0
+80 1 1 0.019020538797954168 0.9811592 0.010528394 0.008312513 1 2 0
+81 1 1 0.015925180791274182 0.98420095 0.011949363 0.0038497108 1 0 2
+83 1 2 2.0406980454630639 0.87002933 0.12993798 3.258634E-05 2 1 0
+84 1 1 0.43994994621280786 0.64406866 0.35437283 0.0015585137 1 2 0
+85 1 1 0.05211593834564536 0.9492188 0.04295896 0.007822319 1 2 0
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+94 1 1 0.064166784413406702 0.93784857 0.058178328 0.003973016 1 2 0
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+118 2 2 8.392686160427524E-05 0.9999161 8.372341E-05 1.07175394E-10 2 1 0
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+139 2 2 0.12014629655188541 0.8867907 0.11317021 3.931525E-05 2 1 0
+140 2 2 0.0054330927887431615 0.99458164 0.0054175323 7.817332E-07 2 1 0
+142 2 2 0.016407972136600949 0.9837259 0.016270882 3.321601E-06 2 1 0
+143 2 2 0.0040641049565237689 0.99594414 0.00405544 2.884136E-07 2 1 0
+146 2 2 0.063336850854471993 0.93862724 0.061356686 1.6151482E-05 2 1 0
+148 2 2 0.013229351377581255 0.9868578 0.013137038 5.0962526E-06 2 1 0
+149 2 2 0.090927626330238626 0.9130838 0.086879514 3.6539095E-05 2 1 0
diff --git a/test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-TrainTest-iris.txt b/test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-TrainTest-iris.txt
new file mode 100644
index 0000000000..1f136318b1
--- /dev/null
+++ b/test/BaselineOutput/SingleRelease/MulticlassLogisticRegression/netcoreapp31/MulticlassLogisticRegression-TrainTest-iris.txt
@@ -0,0 +1,151 @@
+Instance Label Assigned Log-loss #1 Score #2 Score #3 Score #1 Class #2 Class #3 Class
+0 0 0 0.0011148704666220459 0.99888575 0.0011145204 1.3776857E-13 0 1 2
+1 0 0 0.0070600595001449043 0.9929648 0.007035469 5.980848E-12 0 1 2
+2 0 0 0.0023107383603406979 0.9976919 0.002307759 1.30608E-12 0 1 2
+3 0 0 0.0071973505498515739 0.9928285 0.0071717617 1.5530932E-11 0 1 2
+4 0 0 0.00077247645619236299 0.9992278 0.0007718982 9.492881E-14 0 1 2
+5 0 0 0.00070763824108637647 0.9992926 0.00070762733 1.7460614E-13 0 1 2
+6 0 0 0.0015249548965825181 0.9984762 0.0015239433 1.9716312E-12 0 1 2
+7 0 0 0.0023698852189009997 0.9976329 0.0023674963 7.53184E-13 0 1 2
+8 0 0 0.01027010486628236 0.98978245 0.010217615 4.4595366E-11 0 1 2
+9 0 0 0.0076313190029468729 0.9923977 0.0076022604 3.5732832E-12 0 1 2
+10 0 0 0.00077915735755335308 0.99922115 0.0007790165 3.6408105E-14 0 1 2
+11 0 0 0.0034885388576309144 0.99651754 0.0034823413 2.83667E-12 0 1 2
+12 0 0 0.0075292801506795124 0.992499 0.0075008543 4.1048614E-12 0 1 2
+13 0 0 0.0024023278603287428 0.99760056 0.0023991016 1.5549362E-12 0 1 2
+14 0 0 8.1065602621708377E-05 0.99991894 8.0906575E-05 1.6902284E-16 0 1 2
+15 0 0 5.149973917474053E-05 0.9999485 5.1647687E-05 6.3881086E-16 0 1 2
+16 0 0 0.00015259953334155612 0.9998474 0.00015233555 7.692787E-15 0 1 2
+17 0 0 0.0010471460236901183 0.9989534 0.0010464673 2.6370047E-13 0 1 2
+18 0 0 0.0010852740224894391 0.9989153 0.0010850534 7.665227E-14 0 1 2
+19 0 0 0.00050831837325152984 0.9994918 0.00050798786 8.2925454E-14 0 1 2
+20 0 0 0.0050735213310076762 0.9949393 0.005060825 1.1999263E-12 0 1 2
+21 0 0 0.00068950576665922968 0.99931073 0.00068974565 3.0276918E-13 0 1 2
+22 0 0 0.00016690693355841423 0.9998331 0.00016714552 1.24774365E-14 0 1 2
+23 0 0 0.0060968509418567766 0.9939217 0.006078474 3.6398856E-11 0 1 2
+24 0 0 0.011000658927949667 0.9890596 0.010940627 2.9289283E-11 0 1 2
+25 0 0 0.015137646970205936 0.98497635 0.015023758 2.1512653E-11 0 1 2
+26 0 0 0.0030670062937996402 0.9969377 0.003062062 6.018899E-12 0 1 2
+27 0 0 0.0016346217426049894 0.9983667 0.0016332197 2.287227E-13 0 1 2
+28 0 0 0.0016097859543283302 0.9983915 0.0016089765 1.9991067E-13 0 1 2
+29 0 0 0.0072956329818799339 0.9927309 0.007268736 1.3519734E-11 0 1 2
+30 0 0 0.010519988636096064 0.98953515 0.010464923 1.956453E-11 0 1 2
+31 0 0 0.0020780682572110079 0.9979241 0.0020754703 9.251467E-13 0 1 2
+32 0 0 0.0001897991996934539 0.9998102 0.00019020819 2.480733E-15 0 1 2
+33 0 0 8.392686160427524E-05 0.9999161 8.372972E-05 5.0225263E-16 0 1 2
+34 0 0 0.0076313190029468729 0.9923977 0.0076022604 3.5732832E-12 0 1 2
+35 0 0 0.0015668620918511083 0.99843436 0.0015660885 2.637719E-13 0 1 2
+36 0 0 0.00075529720050108832 0.999245 0.00075494894 2.1162744E-14 0 1 2
+37 0 0 0.0076313190029468729 0.9923977 0.0076022604 3.5732832E-12 0 1 2
+38 0 0 0.0048484729816518539 0.99516326 0.0048371274 1.0765147E-11 0 1 2
+39 0 0 0.0023670174117273336 0.9976358 0.0023640736 5.7310146E-13 0 1 2
+40 0 0 0.00071431870928224299 0.99928594 0.0007139834 1.5880794E-13 0 1 2
+41 0 0 0.058616606694306482 0.94306827 0.05693195 1.3674992E-09 0 1 2
+42 0 0 0.0023202972115890941 0.9976824 0.002317787 2.964662E-12 0 1 2
+43 0 0 0.0018701744864162277 0.9981316 0.0018682877 1.1569887E-11 0 1 2
+44 0 0 0.0022153938202876546 0.99778706 0.0022129146 3.5982877E-12 0 1 2
+45 0 0 0.0066394791182328066 0.9933825 0.0066177957 1.5050398E-11 0 1 2
+46 0 0 0.00079442815684458367 0.9992059 0.0007942036 9.45553E-14 0 1 2
+47 0 0 0.0033950556005307988 0.9966107 0.0033896863 3.7430263E-12 0 1 2
+48 0 0 0.00078011177567713298 0.9992202 0.00078014436 4.7848332E-14 0 1 2
+49 0 0 0.0023355318196242821 0.9976672 0.0023323793 6.583122E-13 0 1 2
+50 1 1 0.0060477374096205563 0.9939705 0.0040119863 0.002017312 1 2 0
+51 1 1 0.023358802522272765 0.9769119 0.018574875 0.0045133037 1 2 0
+52 1 1 0.03167006942034075 0.9688262 0.030503416 0.0006706227 1 2 0
+53 1 1 0.085153806889826739 0.918371 0.080720015 0.0009088988 1 2 0
+54 1 1 0.063849951086753426 0.93814576 0.061179116 0.0006753106 1 2 0
+55 1 1 0.089629429284646664 0.9142699 0.08488794 0.00084198435 1 2 0
+56 1 1 0.08483648364691608 0.9186625 0.07830901 0.0030285995 1 2 0
+57 1 1 0.019588462132268222 0.98060215 0.016944302 0.0024533693 1 0 2
+58 1 1 0.0099349166147827024 0.9901143 0.008975584 0.0009100606 1 2 0
+59 1 1 0.092321899949188477 0.9118116 0.08203742 0.006150943 1 2 0
+60 1 1 0.014485823445928967 0.9856186 0.012574332 0.0018072706 1 2 0
+61 1 1 0.045088304308168195 0.95591307 0.037453618 0.006633371 1 2 0
+62 1 1 0.0040779297962239254 0.9959304 0.00350171 0.00056805444 1 2 0
+63 1 1 0.10219954324186892 0.9028494 0.09655303 0.0005976357 1 2 0
+64 1 1 0.04381778370401148 0.95712835 0.040380865 0.0024907354 1 0 2
+65 1 1 0.0080509348386470451 0.9919814 0.004386741 0.0036320223 1 0 2
+66 1 1 0.25712513878794285 0.77327144 0.22504078 0.0016878267 1 2 0
+67 1 1 0.0046958738522607582 0.99531513 0.0024410768 0.002243941 1 0 2
+68 1 1 0.42020607515071323 0.65691143 0.3430131 7.5479635E-05 1 2 0
+69 1 1 0.0088743981357345281 0.99116486 0.006175138 0.002660179 1 2 0
+70 1 2 1.119156119237483 0.67287296 0.32655525 0.00057173334 2 1 0
+71 1 1 0.010574322077426965 0.9894814 0.006254719 0.0042638187 1 0 2
+72 1 1 0.61366271154662444 0.5413644 0.45859504 4.0561474E-05 1 2 0
+73 1 1 0.03377105479336788 0.9667928 0.03281732 0.0003900002 1 2 0
+74 1 1 0.0076222497957970196 0.9924067 0.004714336 0.0028790454 1 2 0
+75 1 1 0.0093469378209280057 0.9906966 0.006279014 0.0030244375 1 2 0
+76 1 1 0.03111313795737956 0.9693659 0.030329114 0.00030520634 1 2 0
+77 1 1 0.36622901478232017 0.693344 0.30640054 0.00025561816 1 2 0
+78 1 1 0.12305449703206547 0.8842155 0.11444254 0.0013420355 1 2 0
+79 1 1 0.017178073477986378 0.9829686 0.016676014 0.00035517136 1 0 2
+80 1 1 0.0099248633048492161 0.9901242 0.007182131 0.002693587 1 2 0
+81 1 1 0.0061249768712999088 0.99389374 0.0037273536 0.0023786854 1 0 2
+82 1 1 0.01018477684487269 0.9898669 0.0059374967 0.0041955533 1 0 2
+83 1 2 1.7848181575763675 0.8321596 0.16782758 1.2941472E-05 2 1 0
+84 1 1 0.40771368629306376 0.6651693 0.33338308 0.0014476714 1 2 0
+85 1 1 0.073538094082338953 0.92910075 0.061388977 0.009510201 1 2 0
+86 1 1 0.02568532133252191 0.97464174 0.023907999 0.0014502493 1 2 0
+87 1 1 0.047872087095895285 0.9532557 0.046538867 0.00020534953 1 2 0
+88 1 1 0.023007854444924543 0.9772548 0.014006073 0.008739044 1 2 0
+89 1 1 0.051250957163473519 0.9500402 0.047992557 0.0019672096 1 2 0
+90 1 1 0.088376539509711596 0.9154161 0.08403013 0.0005538561 1 2 0
+91 1 1 0.054428086534615831 0.9470266 0.051642187 0.0013313727 1 2 0
+92 1 1 0.011075027413584841 0.9889861 0.008220732 0.002793185 1 2 0
+93 1 1 0.01437425449213319 0.98572856 0.011792642 0.002478861 1 0 2
+94 1 1 0.049662849884455844 0.9515502 0.046534896 0.0019149618 1 2 0
+95 1 1 0.013598453800202021 0.9864936 0.00785744 0.0056490703 1 2 0
+96 1 1 0.025311486855273613 0.97500616 0.020882485 0.004111272 1 2 0
+97 1 1 0.01101301311502036 0.9890474 0.00809153 0.002861004 1 2 0
+98 1 1 0.081760169588047965 0.92149293 0.0778774 0.00062952057 1 0 2
+99 1 1 0.023015234494193421 0.9772476 0.018569632 0.0041829064 1 2 0
+100 2 2 0.00011158612051911557 0.9998884 0.00011122978 4.3882338E-09 2 1 0
+101 2 2 0.013729575340933843 0.98636425 0.0136345485 1.266714E-06 2 1 0
+102 2 2 0.010888574253525379 0.9891705 0.010829585 1.6294389E-07 2 1 0
+103 2 2 0.025930890575031128 0.9744024 0.025596872 6.894996E-07 2 1 0
+104 2 2 0.0015621459522632778 0.9984391 0.0015607552 3.6407382E-08 2 1 0
+105 2 2 0.0026502530714843367 0.99735326 0.0026466271 2.7263904E-09 2 1 0
+106 2 2 0.030433677359253137 0.97002476 0.0299635 1.159951E-05 2 1 0
+107 2 2 0.024480795647284091 0.9758164 0.024183862 4.4924544E-08 2 1 0
+108 2 2 0.011575682088038546 0.98849106 0.01150899 3.3041104E-08 2 1 0
+109 2 2 0.0019807750421713521 0.9980212 0.0019784786 1.6299222E-07 2 1 0
+110 2 2 0.1668877744415341 0.8462946 0.15360819 9.722446E-05 2 1 0
+111 2 2 0.031444061158942889 0.96904516 0.030953335 1.3455377E-06 2 1 0
+112 2 2 0.023395533219334613 0.976876 0.023122288 1.6099556E-06 2 1 0
+113 2 2 0.004405951750777239 0.99560374 0.0043958076 3.048161E-07 2 1 0
+114 2 2 0.00051786001087093976 0.9994823 0.00051763566 9.5351375E-08 2 1 0
+115 2 2 0.0073614401801424031 0.9926656 0.0073315674 2.597062E-06 2 1 0
+116 2 2 0.085010512269807784 0.9185026 0.08149283 4.67563E-06 2 1 0
+117 2 2 0.010475174804130676 0.9895795 0.0104205115 1.4943078E-07 2 1 0
+118 2 2 8.392686160427524E-05 0.9999161 8.4219195E-05 7.1131295E-12 2 1 0
+119 2 2 0.14243270625738888 0.8672459 0.13275184 2.228693E-06 2 1 0
+120 2 2 0.0058665359402536059 0.99415064 0.0058489824 4.4902038E-07 2 1 0
+121 2 2 0.011418315077759932 0.9886466 0.011349821 3.4921036E-06 2 1 0
+122 2 2 0.0027408576136189505 0.9972629 0.0027368125 8.6288726E-10 2 1 0
+123 2 2 0.21712063090760428 0.8048329 0.19513033 3.696039E-05 2 1 0
+124 2 2 0.018555186660661513 0.9816159 0.018382292 1.7941145E-06 2 1 0
+125 2 2 0.13317062821825568 0.8753157 0.12468223 2.215571E-06 2 1 0
+126 2 2 0.30929185172157869 0.7339665 0.26592687 0.00010682235 2 1 0
+127 2 2 0.28011468210882728 0.7556971 0.24416323 0.00013955706 2 1 0
+128 2 2 0.0030803390471882929 0.9969244 0.0030754893 6.93042E-08 2 1 0
+129 2 2 0.5607895853823287 0.5707582 0.42923495 6.927903E-06 2 1 0
+130 2 2 0.026384817227264001 0.9739602 0.026039718 7.690115E-08 2 1 0
+131 2 2 0.22129675374786231 0.8014788 0.19851315 7.966131E-06 2 1 0
+132 2 2 0.0015125382870987553 0.9984886 0.0015110222 3.6266687E-08 2 1 0
+133 2 1 0.80010965478119345 0.55066234 0.4492797 5.7917074E-05 1 2 0
+134 2 2 0.10865733918867169 0.89703774 0.10296163 7.100352E-07 2 1 0
+135 2 2 0.0060692055433251568 0.9939492 0.0060508684 4.8325873E-08 2 1 0
+136 2 2 0.0014610229435586669 0.99854004 0.0014594739 3.6343258E-07 2 1 0
+137 2 2 0.085431109138005057 0.9181164 0.08187672 6.785138E-06 2 1 0
+138 2 2 0.31214448263984934 0.7318758 0.26789987 0.00022450527 2 1 0
+139 2 2 0.059142087199894701 0.94257283 0.057418868 8.503209E-06 2 1 0
+140 2 2 0.0018873131884692992 0.99811447 0.0018852964 1.5602878E-07 2 1 0
+141 2 2 0.047074804913225708 0.95401603 0.0459597 2.4441237E-05 2 1 0
+142 2 2 0.013729575340933843 0.98636425 0.0136345485 1.266714E-06 2 1 0
+143 2 2 0.0020265834982710466 0.99797547 0.0020240443 7.197046E-08 2 1 0
+144 2 2 0.0010843193130689751 0.99891627 0.0010831588 1.3605347E-07 2 1 0
+145 2 2 0.014166328018634792 0.98593354 0.0140630165 3.5111582E-06 2 1 0
+146 2 2 0.04495238264372909 0.956043 0.04395422 2.8865643E-06 2 1 0
+147 2 2 0.067826276148074821 0.9344228 0.065563805 1.3508331E-05 2 1 0
+148 2 2 0.005008703171980048 0.9950038 0.0049937237 2.5133447E-06 2 1 0
+149 2 2 0.081482913321255249 0.92174846 0.07823061 2.0680885E-05 2 1 0
diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
index af20d1e272..f038605fd4 100644
--- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
+++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
@@ -146,7 +146,7 @@ public void EarlyStoppingTest()
///
/// Multiclass Logistic Regression test.
///
- [LessThanNetCore30OrNotNetCoreFact("netcoreapp3.1 output differs from Baseline")]
+ [Fact]
[TestCategory("Multiclass")]
[TestCategory("Logistic Regression")]
public void MulticlassLRTest()
@@ -320,7 +320,7 @@ public void BinaryClassifierLogisticRegressionNormTest()
///
///A test for binary classifiers with non-negative coefficients
///
- [LessThanNetCore30OrNotNetCoreAndX64Fact("netcoreapp3.1 and x86 output differs from Baseline")]
+ [X64Fact("x86 output differs from Baseline")]
[TestCategory("Binary")]
public void BinaryClassifierLogisticRegressionNonNegativeTest()
{
@@ -352,7 +352,7 @@ public void BinaryClassifierLogisticRegressionBinNormTest()
///
///A test for binary classifiers
///
- [LessThanNetCore30OrNotNetCoreAndX64Fact("x86 output differs from Baseline and flaky on netcore 3.0")]
+ [X64Fact("x86 output differs from Baseline")]
[TestCategory("Binary")]
public void BinaryClassifierLogisticRegressionGaussianNormTest()
{
@@ -1606,7 +1606,7 @@ public IList GetDatasetsForCalibratorTest()
///
///A test for no calibrators
///
- [LessThanNetCore30OrNotNetCoreFact("netcoreapp3.1 output differs from Baseline")]
+ [Fact]
[TestCategory("Calibrator")]
public void DefaultCalibratorPerceptronTest()
{
@@ -1618,7 +1618,7 @@ public void DefaultCalibratorPerceptronTest()
///
///A test for PAV calibrators
///
- [LessThanNetCore30OrNotNetCoreFact("netcoreapp3.1 output differs from Baseline")]
+ [Fact]
[TestCategory("Calibrator")]
public void PAVCalibratorPerceptronTest()
{
@@ -1630,7 +1630,7 @@ public void PAVCalibratorPerceptronTest()
///
///A test for random calibrators
///
- [LessThanNetCore30OrNotNetCoreAndX64Fact("netcoreapp3.1 and x86 output differs from Baseline")]
+ [X64Fact("x86 output differs from Baseline")]
[TestCategory("Calibrator")]
public void RandomCalibratorPerceptronTest()
{
@@ -2148,7 +2148,7 @@ public sealed partial class TestPredictors
///
///A test for binary classifiers
///
- [LessThanNetCore30OrNotNetCoreFact("netcoreapp3.1 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
[TestCategory("LDSVM")]
public void BinaryClassifierLDSvmTest()
@@ -2162,7 +2162,7 @@ public void BinaryClassifierLDSvmTest()
///
///A test for binary classifiers
///
- [LessThanNetCore30OrNotNetCoreFact("netcoreapp3.1 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
[TestCategory("LDSVM")]
public void BinaryClassifierLDSvmNoBiasTest()
@@ -2201,7 +2201,7 @@ public void MulticlassNaiveBayes()
Done();
}
- [LessThanNetCore30OrNotNetCoreFact("output on .NetCore 3.0 differs. Tracked on issue 3856 in GitHub.")]
+ [Fact]
public void EnsemblesMultiClassBootstrapSelectorTest()
{
var pa = new PredictorAndArgs(new SubComponent("WeightedEnsembleMulticlass", "bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-"), "WE-Bootstrap");
@@ -2298,7 +2298,7 @@ public void EnsemblesRandomSubSpaceSelectorTest()
Done();
}
- [LessThanNetCore30OrNotNetCoreFact("output on .NetCore 3.0 differs. Tracked on issue 3856 in GitHub.")]
+ [Fact]
public void EnsemblesMultiAveragerTest()
{
var pa = new PredictorAndArgs(new SubComponent("WeightedEnsembleMulticlass", "bp=mlr{t-} nm=5 oc=MultiAverage tp=-"), "WE-Average");
@@ -2306,7 +2306,7 @@ public void EnsemblesMultiAveragerTest()
Done();
}
- [LessThanNetCore30OrNotNetCoreFact("output on .NetCore 3.0 differs. Tracked on issue 3856 in GitHub.")]
+ [Fact]
public void EnsemblesMultiVotingCombinerTest()
{
var pa = new PredictorAndArgs(new SubComponent("WeightedEnsembleMulticlass", "bp=mlr{t-} nm=5 oc=MultiVoting tp=-"), "WE-Voting");
@@ -2314,7 +2314,7 @@ public void EnsemblesMultiVotingCombinerTest()
Done();
}
- [LessThanNetCore30OrNotNetCoreFact("output on .NetCore 3.0 differs. Tracked on issue 3856 in GitHub.")]
+ [Fact]
public void EnsemblesMultiStackCombinerTest()
{
var pa = new PredictorAndArgs(new SubComponent("WeightedEnsembleMulticlass", "bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-"), "WE-Stacking");
diff --git a/test/Microsoft.ML.TestFramework/Attributes/LessThanNetCore30OrNotNetCoreAndX64FactAttribute.cs b/test/Microsoft.ML.TestFramework/Attributes/LessThanNetCore30OrNotNetCoreAndX64FactAttribute.cs
deleted file mode 100644
index d2facf6c47..0000000000
--- a/test/Microsoft.ML.TestFramework/Attributes/LessThanNetCore30OrNotNetCoreAndX64FactAttribute.cs
+++ /dev/null
@@ -1,41 +0,0 @@
-// Licensed to the .NET Foundation under one or more agreements.
-// The .NET Foundation licenses this file to you under the MIT license.
-// See the LICENSE file in the project root for more information.
-
-using System;
-using Microsoft.ML.TestFrameworkCommon.Attributes;
-
-namespace Microsoft.ML.TestFramework.Attributes
-{
- ///
- /// A fact for tests requiring x64 environment and either .NET Core version lower than 3.0 or framework other than .NET Core.
- ///
- public sealed class LessThanNetCore30OrNotNetCoreAndX64FactAttribute : EnvironmentSpecificFactAttribute
- {
- public LessThanNetCore30OrNotNetCoreAndX64FactAttribute(string skipMessage) : base(skipMessage)
- {
- }
-
- ///
- protected override bool IsEnvironmentSupported()
- {
- return Environment.Is64BitProcess && AppDomain.CurrentDomain.GetData("FX_PRODUCT_VERSION") == null;
- }
- }
-
- ///
- /// A fact for tests requiring x64 environment and either .NET Core version lower than 3.0 or framework other than .NET Core.
- ///
- public sealed class LessThanNetCore30OrNotNetCore : EnvironmentSpecificFactAttribute
- {
- public LessThanNetCore30OrNotNetCore() : base("Skipping test on .net core version > 3.0 ")
- {
- }
-
- ///
- protected override bool IsEnvironmentSupported()
- {
- return AppDomain.CurrentDomain.GetData("FX_PRODUCT_VERSION") == null;
- }
- }
-}
\ No newline at end of file
diff --git a/test/Microsoft.ML.TestFramework/Attributes/LessThanNetCore30OrNotNetCoreFactAttribute.cs b/test/Microsoft.ML.TestFramework/Attributes/LessThanNetCore30OrNotNetCoreFactAttribute.cs
deleted file mode 100644
index 83df75cd9e..0000000000
--- a/test/Microsoft.ML.TestFramework/Attributes/LessThanNetCore30OrNotNetCoreFactAttribute.cs
+++ /dev/null
@@ -1,25 +0,0 @@
-// Licensed to the .NET Foundation under one or more agreements.
-// The .NET Foundation licenses this file to you under the MIT license.
-// See the LICENSE file in the project root for more information.
-
-using System;
-using Microsoft.ML.TestFrameworkCommon.Attributes;
-
-namespace Microsoft.ML.TestFramework.Attributes
-{
- ///
- /// A fact for tests requiring either .NET Core version lower than 3.0 or framework other than .NET Core.
- ///
- public sealed class LessThanNetCore30OrNotNetCoreFactAttribute : EnvironmentSpecificFactAttribute
- {
- public LessThanNetCore30OrNotNetCoreFactAttribute(string skipMessage) : base(skipMessage)
- {
- }
-
- ///
- protected override bool IsEnvironmentSupported()
- {
- return AppDomain.CurrentDomain.GetData("FX_PRODUCT_VERSION") == null;
- }
- }
-}
\ No newline at end of file
diff --git a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs
index 209c49bed9..ab52fbd17e 100644
--- a/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs
+++ b/test/Microsoft.ML.TestFramework/BaseTestBaseline.cs
@@ -74,7 +74,7 @@ protected BaseTestBaseline(ITestOutputHelper output) : base(output)
// Full paths to the baseline directories.
private string _baselineCommonDir;
private string _baselineBuildStringDir;
- private string _baselineConfigDir;
+ private IEnumerable _baselineConfigDirs;
// The writer to write to test log files.
protected TestLogger TestLogger;
@@ -95,7 +95,7 @@ protected override void Initialize()
_baselineCommonDir = Path.Combine(baselineRootDir, "Common");
_baselineBuildStringDir = Path.Combine(baselineRootDir, BuildString);
- _baselineConfigDir = GetConfigurationDir();
+ _baselineConfigDirs = GetConfigurationDirs();
string logDir = Path.Combine(OutDir, _logRootRelPath);
Directory.CreateDirectory(logDir);
@@ -111,22 +111,43 @@ protected override void Initialize()
ML.AddStandardComponents();
}
- private string GetConfigurationDir()
+ private IEnumerable GetConfigurationDirs()
{
+ // Sometimes different configuration will have combination.
+ // for example: one test can run on windows, have different result both
+ // on x64 vs x86 and dotnet core 3.1 vs others, so we have 4 combination:
+ // x64-netcore3.1, x86-netcore3.1, x64-rest, x86-rest. In some cases x64 vs x86
+ // have different results, in some cases netcore 3.1 vs rest have different results,
+ // the most complicate situation is 12 combinations (x64 vs x86, netcoreapp3.1 vs rest,
+ // win vs linux vs osx) have different results.
+ // So use list of string to return different configurations and test will try to search
+ // through this list and use the one file first found, make sure we don't have baseline file
+ // at different configuration folders.
+ var configurationDirs = new List();
+
if (RuntimeInformation.IsOSPlatform(OSPlatform.OSX))
- return "osx-x64";
+ configurationDirs.Add("osx-x64");
if (RuntimeInformation.IsOSPlatform(OSPlatform.Linux))
- return "linux-x64";
+ configurationDirs.Add("linux-x64");
if (RuntimeInformation.IsOSPlatform(OSPlatform.Windows))
if (Environment.Is64BitProcess)
- return "win-x64";
+ configurationDirs.Add("win-x64");
else
- return "win-x86";
+ configurationDirs.Add("win-x86");
- // We can add other configuration specific strings here for
- // tests that need configuration specific baselines
+ // Use netcore 3.1 result file if necessary.
+ // The small difference comes from CPUMath using different instruction set:
+ // 1. net framework and net core 2.1 uses CpuMathUtils.netstandard that uses SSE instruction set;
+ // 2. net core 3.1 uses CpuMathUtils.netcoreapp that uses AVX, SSE or direct floating point calculation
+ // depending on hardward avaibility.
+ // AVX and SSE generates slightly different result due to nature of floating point math.
+ // So Ideally we should adding AVX support at CPUMath native library,
+ // use below issue to track: https://github.com/dotnet/machinelearning/issues/5044
+ // don't need netcoreapp21 as this is the default case
+ if (AppDomain.CurrentDomain.GetData("FX_PRODUCT_VERSION") != null)
+ configurationDirs.Add("netcoreapp31");
- throw new NotSupportedException("Unknown configuration");
+ return configurationDirs;
}
private void LogTestOutput(object sender, LoggingEventArgs e)
@@ -231,11 +252,15 @@ protected string GetBaselinePath(string subDir, string name)
Contracts.Assert(IsActive);
subDir = subDir ?? string.Empty;
- // first check if a platform specific baseline exists
string baselinePath;
- baselinePath = Path.GetFullPath(Path.Combine(_baselineCommonDir, subDir, _baselineConfigDir, name));
- if (File.Exists(baselinePath))
- return baselinePath;
+
+ // first check if a platform specific baseline exists
+ foreach (var baselineCOnfigDir in _baselineConfigDirs)
+ {
+ baselinePath = Path.GetFullPath(Path.Combine(_baselineCommonDir, subDir, baselineCOnfigDir, name));
+ if (File.Exists(baselinePath))
+ return baselinePath;
+ }
// then check the common folder without a platform dir, and use it if it exists
baselinePath = Path.GetFullPath(Path.Combine(_baselineCommonDir, subDir, name));
@@ -243,9 +268,12 @@ protected string GetBaselinePath(string subDir, string name)
return baselinePath;
// check again for a platform specific dir
- baselinePath = Path.GetFullPath(Path.Combine(_baselineBuildStringDir, subDir, _baselineConfigDir, name));
- if (File.Exists(baselinePath))
- return baselinePath;
+ foreach (var baselineCOnfigDir in _baselineConfigDirs)
+ {
+ baselinePath = Path.GetFullPath(Path.Combine(_baselineBuildStringDir, subDir, baselineCOnfigDir, name));
+ if (File.Exists(baselinePath))
+ return baselinePath;
+ }
return Path.GetFullPath(Path.Combine(_baselineBuildStringDir, subDir, name));
}
@@ -378,6 +406,7 @@ protected bool CheckEqualityCore(string dir, string name, string nameBase, bool
if (!CheckBaseFile(basePath))
return false;
+
bool res = CheckEqualityFromPathsCore(relPath, basePath, outPath, digitsOfPrecision: digitsOfPrecision, parseOption: parseOption);
// No need to keep the raw (unnormalized) output file.
diff --git a/test/Microsoft.ML.TestFramework/TestCommandBase.cs b/test/Microsoft.ML.TestFramework/TestCommandBase.cs
index c136e753af..42edf105b3 100644
--- a/test/Microsoft.ML.TestFramework/TestCommandBase.cs
+++ b/test/Microsoft.ML.TestFramework/TestCommandBase.cs
@@ -1200,7 +1200,7 @@ public void CommandTrainMlrWithLabelNames()
Done();
}
- [LessThanNetCore30OrNotNetCoreFact("netcoreapp3.1 output differs from Baseline")]
+ [Fact]
[TestCategory(Cat), TestCategory("Multiclass"), TestCategory("Logistic Regression")]
public void CommandTrainMlrWithStats()
{
diff --git a/test/Microsoft.ML.Tests/TrainerEstimators/LbfgsTests.cs b/test/Microsoft.ML.Tests/TrainerEstimators/LbfgsTests.cs
index ea7a84de43..cea943ea3d 100644
--- a/test/Microsoft.ML.Tests/TrainerEstimators/LbfgsTests.cs
+++ b/test/Microsoft.ML.Tests/TrainerEstimators/LbfgsTests.cs
@@ -177,7 +177,7 @@ public void TestMLRNoStats()
Done();
}
- [LessThanNetCore30OrNotNetCore]
+ [Fact]
public void TestMLRWithStats()
{
(IEstimator pipe, IDataView dataView) = GetMulticlassPipeline();
@@ -198,8 +198,13 @@ public void TestMLRWithStats()
var stats = modelParams.Statistics;
Assert.NotNull(stats);
+#if NETCOREAPP3_1
+ CompareNumbersWithTolerance(stats.Deviance, 45.79, digitsOfPrecision: 2);
+ CompareNumbersWithTolerance(stats.NullDeviance, 329.58, digitsOfPrecision: 2);
+#else
CompareNumbersWithTolerance(stats.Deviance, 45.35, digitsOfPrecision: 2);
CompareNumbersWithTolerance(stats.NullDeviance, 329.58, digitsOfPrecision: 2);
+#endif
//Assert.Equal(14, stats.ParametersCount);
Assert.Equal(150, stats.TrainingExampleCount);
};