diff --git a/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..8018c1a230
--- /dev/null
+++ b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,53 @@
+maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise
+Warning: Skipped 16 instances with missing features during training
+Processed 683 instances
+Binning and forming Feature objects
+Starting to train ...
+Training calibrator.
+TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      227 |       14 | 0.9419
+ negative ||       13 |      445 | 0.9716
+          ||======================
+Precision ||   0.9458 |   0.9695 |
+OVERALL 0/1 ACCURACY: 0.961373
+LOG LOSS/instance:  0.145652
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 84.326961
+AUC:                0.991198
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.991198 (0.0000)
+Accuracy:           0.961373 (0.0000)
+Positive precision: 0.945833 (0.0000)
+Positive recall:    0.941909 (0.0000)
+Negative precision: 0.969499 (0.0000)
+Negative recall:    0.971616 (0.0000)
+Log-loss:           0.145652 (0.0000)
+Log-loss reduction: 84.326961 (0.0000)
+F1 Score:           0.943867 (0.0000)
+AUPRC:              0.971819 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'FastTree data preparation' started.
+[1] 'FastTree data preparation' finished in %Time%.
+[2] 'FastTree in-memory bins initialization' started.
+[2] 'FastTree in-memory bins initialization' finished in %Time%.
+[3] 'FastTree feature conversion' started.
+[3] 'FastTree feature conversion' finished in %Time%.
+[4] 'GAM training' started.
+[4] 'GAM training' finished in %Time%.
+[5] 'Saving model' started.
+[5] 'Saving model' finished in %Time%.
diff --git a/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..459ab02427
--- /dev/null
+++ b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+BinaryClassificationGamTrainer
+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	
+0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1		
+
diff --git a/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..81f0e6be3e
--- /dev/null
+++ b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
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diff --git a/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..0b73c5c0ad
--- /dev/null
+++ b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,49 @@
+maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise on disk
+Warning: 16 of 699 examples will be skipped due to missing feature values
+Processed 683 instances
+Binning and forming Feature objects
+Starting to train ...
+Training calibrator.
+TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      227 |       14 | 0.9419
+ negative ||       13 |      445 | 0.9716
+          ||======================
+Precision ||   0.9458 |   0.9695 |
+OVERALL 0/1 ACCURACY: 0.961373
+LOG LOSS/instance:  0.145652
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 84.326961
+AUC:                0.991198
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.991198 (0.0000)
+Accuracy:           0.961373 (0.0000)
+Positive precision: 0.945833 (0.0000)
+Positive recall:    0.941909 (0.0000)
+Negative precision: 0.969499 (0.0000)
+Negative recall:    0.971616 (0.0000)
+Log-loss:           0.145652 (0.0000)
+Log-loss reduction: 84.326961 (0.0000)
+F1 Score:           0.943867 (0.0000)
+AUPRC:              0.971819 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'FastTree disk-based bins initialization' started.
+[1] 'FastTree disk-based bins initialization' finished in %Time%.
+[2] 'GAM training' started.
+[2] 'GAM training' finished in %Time%.
+[3] 'Saving model' started.
+[3] 'Saving model' finished in %Time%.
diff --git a/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..0f52bc9a93
--- /dev/null
+++ b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+BinaryClassificationGamTrainer
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/dt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	+	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1	/dt:+	
+
diff --git a/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..81f0e6be3e
--- /dev/null
+++ b/ZBaselines/SingleDebug/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
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diff --git a/ZBaselines/SingleDebug/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-out.txt b/ZBaselines/SingleDebug/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..f3d444d231
--- /dev/null
+++ b/ZBaselines/SingleDebug/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,50 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise on disk
+Warning: 16 of 699 examples will be skipped due to missing feature values
+Processed 683 instances
+Binning and forming Feature objects
+Reserved memory for tree learner: 3852 bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      240 |        1 | 0.9959
+ negative ||       13 |      445 | 0.9716
+          ||======================
+Precision ||   0.9486 |   0.9978 |
+OVERALL 0/1 ACCURACY: 0.979971
+LOG LOSS/instance:  0.092572
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 90.038683
+AUC:                0.995370
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995370 (0.0000)
+Accuracy:           0.979971 (0.0000)
+Positive precision: 0.948617 (0.0000)
+Positive recall:    0.995851 (0.0000)
+Negative precision: 0.997758 (0.0000)
+Negative recall:    0.971616 (0.0000)
+Log-loss:           0.092572 (0.0000)
+Log-loss reduction: 90.038683 (0.0000)
+F1 Score:           0.971660 (0.0000)
+AUPRC:              0.970606 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'FastTree disk-based bins initialization' started.
+[1] 'FastTree disk-based bins initialization' finished in %Time%.
+[2] 'FastTree training' started.
+[2] 'FastTree training' finished in %Time%.
+[3] 'Saving model' started.
+[3] 'Saving model' finished in %Time%.
diff --git a/ZBaselines/SingleDebug/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-rp.txt b/ZBaselines/SingleDebug/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..bfb88c7533
--- /dev/null
+++ b/ZBaselines/SingleDebug/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+FastTreeBinaryClassification
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lr	/dt	/nl	/mil	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.99537	0.979971	0.948617	0.995851	0.997758	0.971616	0.092572	90.03868	0.97166	0.970606	0.25	+	5	5	20	FastTreeBinaryClassification	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1	/lr:0.25;/dt:+;/nl:5;/mil:5;/iter:20	
+
diff --git a/ZBaselines/SingleDebug/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt b/ZBaselines/SingleDebug/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..67c3133c67
--- /dev/null
+++ b/ZBaselines/SingleDebug/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
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diff --git a/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..8018c1a230
--- /dev/null
+++ b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,53 @@
+maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise
+Warning: Skipped 16 instances with missing features during training
+Processed 683 instances
+Binning and forming Feature objects
+Starting to train ...
+Training calibrator.
+TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      227 |       14 | 0.9419
+ negative ||       13 |      445 | 0.9716
+          ||======================
+Precision ||   0.9458 |   0.9695 |
+OVERALL 0/1 ACCURACY: 0.961373
+LOG LOSS/instance:  0.145652
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 84.326961
+AUC:                0.991198
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.991198 (0.0000)
+Accuracy:           0.961373 (0.0000)
+Positive precision: 0.945833 (0.0000)
+Positive recall:    0.941909 (0.0000)
+Negative precision: 0.969499 (0.0000)
+Negative recall:    0.971616 (0.0000)
+Log-loss:           0.145652 (0.0000)
+Log-loss reduction: 84.326961 (0.0000)
+F1 Score:           0.943867 (0.0000)
+AUPRC:              0.971819 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'FastTree data preparation' started.
+[1] 'FastTree data preparation' finished in %Time%.
+[2] 'FastTree in-memory bins initialization' started.
+[2] 'FastTree in-memory bins initialization' finished in %Time%.
+[3] 'FastTree feature conversion' started.
+[3] 'FastTree feature conversion' finished in %Time%.
+[4] 'GAM training' started.
+[4] 'GAM training' finished in %Time%.
+[5] 'Saving model' started.
+[5] 'Saving model' finished in %Time%.
diff --git a/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..459ab02427
--- /dev/null
+++ b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+BinaryClassificationGamTrainer
+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	
+0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer dout=%Output% data=%Data% out=%Output% seed=1		
+
diff --git a/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..81f0e6be3e
--- /dev/null
+++ b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Probability	Log-loss	Assigned
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diff --git a/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..0b73c5c0ad
--- /dev/null
+++ b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,49 @@
+maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise on disk
+Warning: 16 of 699 examples will be skipped due to missing feature values
+Processed 683 instances
+Binning and forming Feature objects
+Starting to train ...
+Training calibrator.
+TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      227 |       14 | 0.9419
+ negative ||       13 |      445 | 0.9716
+          ||======================
+Precision ||   0.9458 |   0.9695 |
+OVERALL 0/1 ACCURACY: 0.961373
+LOG LOSS/instance:  0.145652
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 84.326961
+AUC:                0.991198
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.991198 (0.0000)
+Accuracy:           0.961373 (0.0000)
+Positive precision: 0.945833 (0.0000)
+Positive recall:    0.941909 (0.0000)
+Negative precision: 0.969499 (0.0000)
+Negative recall:    0.971616 (0.0000)
+Log-loss:           0.145652 (0.0000)
+Log-loss reduction: 84.326961 (0.0000)
+F1 Score:           0.943867 (0.0000)
+AUPRC:              0.971819 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'FastTree disk-based bins initialization' started.
+[1] 'FastTree disk-based bins initialization' finished in %Time%.
+[2] 'GAM training' started.
+[2] 'GAM training' finished in %Time%.
+[3] 'Saving model' started.
+[3] 'Saving model' finished in %Time%.
diff --git a/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..0f52bc9a93
--- /dev/null
+++ b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+BinaryClassificationGamTrainer
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/dt	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.991198	0.961373	0.945833	0.941909	0.969499	0.971616	0.145652	84.32696	0.943867	0.971819	+	BinaryClassificationGamTrainer	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=BinaryClassificationGamTrainer{dt+} dout=%Output% data=%Data% out=%Output% seed=1	/dt:+	
+
diff --git a/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..81f0e6be3e
--- /dev/null
+++ b/ZBaselines/SingleRelease/BinaryClassificationGamTrainer/BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Probability	Log-loss	Assigned
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+1	0	0.036960315	0.7732779	2.1410029334319507	1
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diff --git a/ZBaselines/SingleRelease/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-out.txt b/ZBaselines/SingleRelease/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..f3d444d231
--- /dev/null
+++ b/ZBaselines/SingleRelease/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,50 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise on disk
+Warning: 16 of 699 examples will be skipped due to missing feature values
+Processed 683 instances
+Binning and forming Feature objects
+Reserved memory for tree learner: 3852 bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO:	0.3448 (241.0/(241.0+458.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      240 |        1 | 0.9959
+ negative ||       13 |      445 | 0.9716
+          ||======================
+Precision ||   0.9486 |   0.9978 |
+OVERALL 0/1 ACCURACY: 0.979971
+LOG LOSS/instance:  0.092572
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 90.038683
+AUC:                0.995370
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995370 (0.0000)
+Accuracy:           0.979971 (0.0000)
+Positive precision: 0.948617 (0.0000)
+Positive recall:    0.995851 (0.0000)
+Negative precision: 0.997758 (0.0000)
+Negative recall:    0.971616 (0.0000)
+Log-loss:           0.092572 (0.0000)
+Log-loss reduction: 90.038683 (0.0000)
+F1 Score:           0.971660 (0.0000)
+AUPRC:              0.970606 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'FastTree disk-based bins initialization' started.
+[1] 'FastTree disk-based bins initialization' finished in %Time%.
+[2] 'FastTree training' started.
+[2] 'FastTree training' finished in %Time%.
+[3] 'Saving model' started.
+[3] 'Saving model' finished in %Time%.
diff --git a/ZBaselines/SingleRelease/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-rp.txt b/ZBaselines/SingleRelease/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..bfb88c7533
--- /dev/null
+++ b/ZBaselines/SingleRelease/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer-rp.txt
@@ -0,0 +1,4 @@
+FastTreeBinaryClassification
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lr	/dt	/nl	/mil	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.99537	0.979971	0.948617	0.995851	0.997758	0.971616	0.092572	90.03868	0.97166	0.970606	0.25	+	5	5	20	FastTreeBinaryClassification	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} cache=- dout=%Output% loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=%Data% out=%Output% seed=1	/lr:0.25;/dt:+;/nl:5;/mil:5;/iter:20	
+
diff --git a/ZBaselines/SingleRelease/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt b/ZBaselines/SingleRelease/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..67c3133c67
--- /dev/null
+++ b/ZBaselines/SingleRelease/FastTreeBinaryClassification/FastTreeDisk-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Probability	Log-loss	Assigned
+0	0	-9.42309	0.00891076	0.012913128393812673	0
+1	0	-0.780937254	0.4036045	0.74565874084578909	0
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+3	0	-0.8294407	0.3977808	0.73163940393192184	0
+4	0	-9.42309	0.00891076	0.012913128393812673	0
+5	1	8.721046	0.9873894	0.018308958497793435	1
+6	0	-2.73920369	0.202684179	0.32677679825805495	0
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+11	0	-9.42309	0.00891076	0.012913128393812673	0
+12	1	2.61111	0.7867684	0.34598912343179539	1
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diff --git a/src/Microsoft.ML.FastTree/FastTree.cs b/src/Microsoft.ML.FastTree/FastTree.cs
index 3f5c2b44f3..00060c44b5 100644
--- a/src/Microsoft.ML.FastTree/FastTree.cs
+++ b/src/Microsoft.ML.FastTree/FastTree.cs
@@ -1545,10 +1545,6 @@ private Dataset Construct(RoleMappedData examples, ref int numExamples, int maxB
                                             {
                                                 flocks.Add(CreateOneHotFlockCategorical(ch, pending, binnedValues,
                                                     lastOn, true));
-
-                                                if (FeatureMap == null)
-                                                    FeatureMap = Enumerable.Range(0, NumFeatures)
-                                                        .Where(f => BinUpperBounds[f].Length > 1).ToArray();
                                             }
                                             iFeature = CategoricalFeatureIndices[catRangeIndex + 1] + 1;
                                             catRangeIndex += 2;
@@ -1556,11 +1552,12 @@ private Dataset Construct(RoleMappedData examples, ref int numExamples, int maxB
                                         else
                                         {
                                             GetFeatureValues(cursor, iFeature, getter, ref temp, ref doubleTemp, copier);
-
-                                            double[] upperBounds = BinUpperBounds[iFeature];
+                                            double[] upperBounds = BinUpperBounds[iFeature++];
                                             Host.AssertValue(upperBounds);
+                                            if (upperBounds.Length == 1)
+                                                continue; //trivial feature, skip it.
+
                                             flocks.Add(CreateSingletonFlock(ch, ref doubleTemp, binnedValues, upperBounds));
-                                            iFeature++;
                                         }
                                     }
                                 }
@@ -1571,10 +1568,16 @@ private Dataset Construct(RoleMappedData examples, ref int numExamples, int maxB
                                         GetFeatureValues(cursor, i, getter, ref temp, ref doubleTemp, copier);
                                         double[] upperBounds = BinUpperBounds[i];
                                         Host.AssertValue(upperBounds);
+                                        if (upperBounds.Length == 1)
+                                            continue; //trivial feature, skip it.
+
                                         flocks.Add(CreateSingletonFlock(ch, ref doubleTemp, binnedValues, upperBounds));
                                     }
                                 }
 
+                                Contracts.Assert(FeatureMap == null);
+
+                                FeatureMap = Enumerable.Range(0, NumFeatures).Where(f => BinUpperBounds[f].Length > 1).ToArray();
                                 features = flocks.ToArray();
                             }
                         }
diff --git a/src/Microsoft.ML.FastTree/GamTrainer.cs b/src/Microsoft.ML.FastTree/GamTrainer.cs
index affcafbde6..bc16a07338 100644
--- a/src/Microsoft.ML.FastTree/GamTrainer.cs
+++ b/src/Microsoft.ML.FastTree/GamTrainer.cs
@@ -574,8 +574,7 @@ protected internal GamPredictorBase(IHostEnvironment env, string name, int input
         {
             Host.CheckValue(trainSet, nameof(trainSet));
             Host.CheckParam(trainSet.NumFeatures <= inputLength, nameof(inputLength), "Must be at least as large as dataset number of features");
-            Host.CheckValue(featureMap, nameof(featureMap));
-            Host.CheckParam(featureMap.Length == trainSet.NumFeatures, nameof(featureMap), "Not of right size");
+            Host.CheckParam(featureMap == null || featureMap.Length == trainSet.NumFeatures, nameof(featureMap), "Not of right size");
             Host.CheckValue(binEffects, nameof(binEffects));
             Host.CheckParam(binEffects.Length == trainSet.NumFeatures, nameof(binEffects), "Not of right size");
 
@@ -584,12 +583,17 @@ protected internal GamPredictorBase(IHostEnvironment env, string name, int input
             _numFeatures = binEffects.Length;
             _inputType = new VectorType(NumberType.Float, _inputLength);
             _featureMap = featureMap;
-            _inputFeatureToDatasetFeatureMap = new Dictionary<int, int>(featureMap.Length);
-            for (int i = 0; i < featureMap.Length; i++)
+
+            //No features were filtered.
+            if (_featureMap == null)
+                _featureMap = Utils.GetIdentityPermutation(trainSet.NumFeatures);
+
+            _inputFeatureToDatasetFeatureMap = new Dictionary<int, int>(_featureMap.Length);
+            for (int i = 0; i < _featureMap.Length; i++)
             {
-                Host.CheckParam(0 <= featureMap[i] && featureMap[i] < inputLength, nameof(featureMap), "Contains out of range feature vaule");
-                Host.CheckParam(!_inputFeatureToDatasetFeatureMap.ContainsValue(featureMap[i]), nameof(featureMap), "Contains duplicate mappings");
-                _inputFeatureToDatasetFeatureMap[featureMap[i]] = i;
+                Host.CheckParam(0 <= _featureMap[i] && _featureMap[i] < inputLength, nameof(_featureMap), "Contains out of range feature vaule");
+                Host.CheckParam(!_inputFeatureToDatasetFeatureMap.ContainsValue(_featureMap[i]), nameof(_featureMap), "Contains duplicate mappings");
+                _inputFeatureToDatasetFeatureMap[_featureMap[i]] = i;
             }
 
             //keep only bin effect and upperbounds where the effect changes.
diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
index a1cd148d5f..4a93d3468d 100644
--- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
+++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
@@ -390,7 +390,8 @@ public void FastTreeBinaryClassificationTest()
         {
             RunMTAThread(() =>
             {
-                var learners = new[] { TestLearners.FastTreeClassfier, TestLearners.FastTreeDropoutClassfier, TestLearners.FastTreeBsrClassfier };
+                var learners = new[] { TestLearners.FastTreeClassfier, TestLearners.FastTreeDropoutClassfier,
+                    TestLearners.FastTreeBsrClassfier, TestLearners.FastTreeClassfierDisk };
                 var binaryClassificationDatasets = new List<TestDataset> { TestDatasets.breastCancerPipe};
                 foreach (var learner in learners)
                 {
@@ -535,13 +536,13 @@ public void GamRegressionTest()
             Done();
         }
 
-        [Fact(Skip = "Need CoreTLC specific baseline update")]
+        [Fact]
         [TestCategory("FastTree")]
         public void GamBinaryClassificationTest()
         {
             RunMTAThread(() =>
             {
-                var learners = new[] { TestLearners.BinaryClassificationGamTrainer };
+                var learners = new[] { TestLearners.BinaryClassificationGamTrainer, TestLearners.BinaryClassificationGamTrainerDiskTranspose };
                 var datasets = GetDatasetsForBinaryClassifierBaseTest();
 
                 foreach (var learner in learners)
diff --git a/test/Microsoft.ML.TestFramework/Learners.cs b/test/Microsoft.ML.TestFramework/Learners.cs
index 2a9cc14f1c..0fcced53e5 100644
--- a/test/Microsoft.ML.TestFramework/Learners.cs
+++ b/test/Microsoft.ML.TestFramework/Learners.cs
@@ -374,6 +374,13 @@ static TestLearnersBase()
             BaselineProgress = true,
         };
 
+        public static PredictorAndArgs BinaryClassificationGamTrainerDiskTranspose = new PredictorAndArgs
+        {
+            Trainer = new SubComponent("BinaryClassificationGamTrainer", "dt+"),
+            Tag = "BinaryClassificationGamTrainerDiskTranspose",
+            BaselineProgress = true,
+        };
+
         // New.
         public static PredictorAndArgs QuantileRegressionScorer = new PredictorAndArgs
         {