diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt
new file mode 100644
index 0000000000..0b7729932c
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat-out.txt
@@ -0,0 +1,61 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise
+Processed 500 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 55 | 60 | 0.4783
+ negative || 17 | 368 | 0.9558
+ ||======================
+Precision || 0.7639 | 0.8598 |
+OVERALL 0/1 ACCURACY: 0.846000
+LOG LOSS/instance: 0.481805
+Test-set entropy (prior Log-Loss/instance): 0.778011
+LOG-LOSS REDUCTION (RIG): 0.380722
+AUC: 0.893281
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.893281 (0.0000)
+Accuracy: 0.846000 (0.0000)
+Positive precision: 0.763889 (0.0000)
+Positive recall: 0.478261 (0.0000)
+Negative precision: 0.859813 (0.0000)
+Negative recall: 0.955844 (0.0000)
+Log-loss: 0.481805 (0.0000)
+Log-loss reduction: 0.380722 (0.0000)
+F1 Score: 0.588235 (0.0000)
+AUPRC: 0.738040 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'Building term dictionary' started.
+[1] (%Time%) 500 examples Total Terms: 76
+[1] 'Building term dictionary' finished in %Time%.
+[2] 'FastTree data preparation' started.
+[2] 'FastTree data preparation' finished in %Time%.
+[3] 'FastTree in-memory bins initialization' started.
+[3] 'FastTree in-memory bins initialization' finished in %Time%.
+[4] 'FastTree feature conversion' started.
+[4] 'FastTree feature conversion' finished in %Time%.
+[5] 'FastTree data preparation #2' started.
+[5] 'FastTree data preparation #2' finished in %Time%.
+[6] 'FastTree feature conversion #2' started.
+[6] 'FastTree feature conversion #2' finished in %Time%.
+[7] 'FastTree training' started.
+[7] 'FastTree training' finished in %Time%.
+[8] 'Saving model' started.
+[8] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt
new file mode 100644
index 0000000000..086e8f83eb
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat-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 /nl /mil /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.893281 0.846 0.763889 0.478261 0.859813 0.955844 0.481805 0.380722 0.588235 0.73804 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} dout=%Output% loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat} /lr:0.25;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt
new file mode 100644
index 0000000000..f0ad632661
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt
@@ -0,0 +1,30 @@
+
+Per-feature gain summary for the boosted tree ensemble:
+ marital-status.Married-civ-spouse 1
+ occupation.Prof-specialty 0.499978104303624
+ occupation.Exec-managerial 0.434935442345068
+ marital-status.Never-married 0.267286281351726
+ education.Doctorate 0.255205743594169
+ Workclass.Self-emp-inc 0.218913886097949
+ ethnicity.Asian-Pac-Islander 0.198007032992433
+ relationship.Husband 0.187623229061434
+ Workclass.Local-gov 0.186017717229329
+ native-country-region.Mexico 0.177614308223737
+ education.Bachelors 0.170936054067049
+ education.Masters 0.169804646788063
+ occupation.Farming-fishing 0.145811380173079
+ ethnicity.Black 0.137140760481446
+ occupation.Transport-moving 0.13022579853358
+ occupation.Other-service 0.12589916923037
+ education.Some-college 0.116092176092999
+ Workclass.Private 0.109401738173027
+ Workclass.? 0.109103172155946
+ education.Assoc-acdm 0.100323599151049
+ Workclass.Self-emp-not-inc 0.0973694292542764
+ relationship.Own-child 0.0957862810994456
+ education.11th 0.0905382862951382
+ occupation.Tech-support 0.0778673683865196
+ education.HS-grad 0.0727543074440302
+ education.7th-8th 0.0727320100839907
+ marital-status.Widowed 0.0634429881388435
+ Workclass.State-gov 0.0535351814497221
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat.ini b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat.ini
new file mode 100644
index 0000000000..62a763ac85
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat.ini
@@ -0,0 +1,444 @@
+[TreeEnsemble]
+Inputs=28
+Evaluators=22
+
+[Input:1]
+Name=marital-status.Married-civ-spouse
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:2]
+Name=occupation.Prof-specialty
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:3]
+Name=occupation.Exec-managerial
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:4]
+Name=Workclass.Local-gov
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:5]
+Name=Workclass.Self-emp-inc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:6]
+Name=occupation.Transport-moving
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:7]
+Name=education.Doctorate
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:8]
+Name=native-country-region.Mexico
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:9]
+Name=ethnicity.Asian-Pac-Islander
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:10]
+Name=education.Masters
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:11]
+Name=marital-status.Never-married
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:12]
+Name=education.Bachelors
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:13]
+Name=relationship.Husband
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:14]
+Name=occupation.Other-service
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:15]
+Name=occupation.Farming-fishing
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:16]
+Name=Workclass.?
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:17]
+Name=education.Assoc-acdm
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:18]
+Name=relationship.Own-child
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:19]
+Name=education.11th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:20]
+Name=education.7th-8th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:21]
+Name=marital-status.Widowed
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:22]
+Name=occupation.Tech-support
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:23]
+Name=ethnicity.Black
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:24]
+Name=Workclass.Private
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:25]
+Name=education.Some-college
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:26]
+Name=Workclass.State-gov
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:27]
+Name=education.HS-grad
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:28]
+Name=Workclass.Self-emp-not-inc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Evaluator:1]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:3 I:4
+SplitGain=4.7088298291299 1.06377951357393 0.899536203027717 0.193365396473055
+GainPValue=5.502e-18 4.072e-05 1.661e-04 8.190e-02
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.902621722846442 -0.398692810457516 0.529411764705882 0.3 0.333333333333333
+
+[Evaluator:2]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:3 I:5
+SplitGain=2.68221454626846 0.600433237671575 0.506893143405883 0.162368897472373
+GainPValue=1.465e-13 1.375e-03 3.487e-03 9.826e-02
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.715974876107119 -0.316545167938715 0.404835898095259 0.226409099860694 0.192149912205831
+
+[Evaluator:3]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:3 I:6
+SplitGain=1.56391762628542 0.341083155599012 0.286617805766863 0.150103158180425
+GainPValue=7.675e-10 1.359e-02 2.495e-02 1.038e-01
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.608372582763175 -0.258035791447291 0.316671655130532 0.172098771587666 0.162245090633324
+
+[Evaluator:4]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:7 I:8 I:9
+SplitGain=0.921177056210434 0.216495044773013 0.182674065452883 0.184429412606235
+GainPValue=5.682e-07 4.605e-02 6.854e-02 6.845e-02
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.533394084868596 -0.0365412115064725 0.767507723671181 -0.807345097851901 -0.792923562512626
+
+[Evaluator:5]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:10 I:10
+SplitGain=0.54584992540154 0.161631049940655 0.181779609060389 0.121620306796041
+GainPValue=6.293e-05 8.146e-02 6.860e-02 1.022e-01
+LTEChild=-1 2 -2 -3
+GTChild=1 3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.474168987477157 -0.110251340122924 0.404079806525013 0.619961193883822 -0.200925527365898
+
+[Evaluator:6]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:11 I:7 I:12 I:5
+SplitGain=0.367633930394679 0.213143059100004 0.190789108308404 0.280078042783584
+GainPValue=7.544e-04 2.811e-02 3.817e-02 1.040e-02
+LTEChild=1 2 3 -1
+GTChild=-2 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.179685812660895 -0.516973244322403 0.922082259591498 0.197508039598293 0.428240071335113
+
+[Evaluator:7]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:13 I:14 I:15 I:16
+SplitGain=0.259824456454265 0.166786940892928 0.131082372857944 0.125253846023304
+GainPValue=3.816e-03 7.227e-02 1.144e-01 1.242e-01
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.343597860992268 0.0828074025474215 -0.540642572611145 -0.51403271076466 -0.483716869431995
+
+[Evaluator:8]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:11 I:3 I:4 I:2
+SplitGain=0.176402557303149 0.15355115254815 0.170738172125627 0.167405941423813
+GainPValue=1.564e-02 5.724e-02 7.675e-02 4.134e-02
+LTEChild=1 3 -3 -1
+GTChild=-2 2 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.140335333109393 -0.452275730955236 0.280989054889103 -0.615571442420238 0.221048660017777
+
+[Evaluator:9]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:7 I:13 I:8 I:2
+SplitGain=0.123387566949866 0.110591363559061 0.0890196337984766 0.0816924271572343
+GainPValue=4.079e-02 5.304e-02 1.875e-01 2.072e-02
+LTEChild=1 3 -3 -1
+GTChild=-2 2 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.340272863195906 0.888347626526744 0.020337116804746 -0.658814447194627 0.137269529227658
+
+[Evaluator:10]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:11 I:9 I:3 I:2
+SplitGain=0.0837195677281312 0.114081595842885 0.0807428963946195 0.107732051438305
+GainPValue=8.962e-02 9.708e-02 1.621e-01 9.891e-02
+LTEChild=1 2 3 -1
+GTChild=-2 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.0826366960049043 -0.393341902124449 -0.622344255099724 0.176267261340734 0.235244668856847
+
+[Evaluator:11]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:17 I:7 I:9 I:2
+SplitGain=0.0719823743813122 0.0681058368523495 0.0555684916763044 0.0775041237055863
+GainPValue=1.131e-01 1.231e-01 1.649e-01 1.014e-01
+LTEChild=1 2 3 -1
+GTChild=-2 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.105427114391291 0.641599955006855 0.832088728975106 -0.552057370572801 0.178601133687322
+
+[Evaluator:12]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:18 I:19 I:20 I:21
+SplitGain=0.0669380455452111 0.0549676544868168 0.0556631135291226 0.0423529410997556
+GainPValue=1.243e-01 2.002e-01 2.023e-01 2.686e-01
+LTEChild=1 2 3 -1
+GTChild=-2 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=0.0294153161974253 -0.474471659717845 -0.4315348185619 -0.419563119605114 -0.569922145586662
+
+[Evaluator:13]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:22 I:12 I:11
+SplitGain=0.0537932899768729 0.0638009740965006 0.0589966622242501 0.0974754616503191
+GainPValue=1.666e-01 2.514e-01 1.820e-02 5.193e-02
+LTEChild=2 -2 -1 -4
+GTChild=1 -3 3 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.319497290449836 0.0156591648943956 0.517083582167814 0.635073686859611 -0.264018319100888
+
+[Evaluator:14]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:3 I:23 I:24 I:5
+SplitGain=0.0414855695096175 0.124269959052185 0.0735784578497881 0.0618228317231743
+GainPValue=2.223e-01 7.246e-02 1.623e-01 1.243e-01
+LTEChild=3 2 -2 -1
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.0844903401344458 -0.23989276656167 1.03875130077975 0.171931769129017 0.253815687961854
+
+[Evaluator:15]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:7 I:9 I:1 I:15
+SplitGain=0.0400280155049618 0.0366700190445627 0.0466542489578826 0.0556553939443772
+GainPValue=2.281e-01 2.502e-01 1.958e-01 2.877e-01
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.188942799100547 0.849620225812268 -0.466876127755269 0.0514388497846418 -0.378620176457517
+
+[Evaluator:16]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:8 I:17 I:6 I:25
+SplitGain=0.0369720876138535 0.0339241030178877 0.0283443027229304 0.141815016010588
+GainPValue=2.451e-01 2.695e-01 3.140e-01 8.965e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.032709544041503 -0.58584044968655 0.47681389705317 0.404126843562897 -0.761182503845142
+
+[Evaluator:17]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:18 I:19 I:26 I:27
+SplitGain=0.0296054320996201 0.0312866652485517 0.0301574677788823 0.0556972479007501
+GainPValue=2.956e-01 3.236e-01 3.384e-01 1.581e-01
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=0.034962626335391 -0.417578532578969 -0.365761281852063 0.0154901049784514 -0.725517842066042
+
+[Evaluator:18]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:8 I:11 I:15 I:28
+SplitGain=0.0232838606683857 0.0265119320499235 0.0369793203414513 0.0366872392283748
+GainPValue=3.523e-01 3.253e-01 3.338e-01 3.384e-01
+LTEChild=1 2 3 -1
+GTChild=-2 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=0.0582422803088705 -0.565666845256837 -0.251314640848361 -0.342515720855204 -0.175151444012064
+
+[Evaluator:19]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:7 I:9 I:2 I:28
+SplitGain=0.0241664682909133 0.0218013884377193 0.0291172390169779 0.0630739374903595
+GainPValue=3.422e-01 3.690e-01 3.008e-01 1.977e-01
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.0302677118486371 0.807876876723166 -0.389917607805295 0.253632461416754 -0.464399062713166
+
+[Evaluator:20]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:3 I:23 I:24 I:12
+SplitGain=0.021690329791823 0.0736316937652476 0.0523618523688019 0.0576707183200445
+GainPValue=3.667e-01 1.522e-01 2.273e-01 2.021e-01
+LTEChild=-1 2 -2 -4
+GTChild=1 -3 3 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.0316533680169522 -0.200974024741109 0.861719636728227 -0.00163088964396286 0.52356838088468
+
+[Evaluator:21]
+EvaluatorType=Aggregator
+NumNodes=20
+Nodes=E:1 E:2 E:3 E:4 E:5 E:6 E:7 E:8 E:9 E:10 E:11 E:12 E:13 E:14 E:15 E:16 E:17 E:18 E:19 E:20
+Weights=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+Bias=0
+Type=Linear
+
+[Comments]
+C:0=Regression Tree Ensemble
+C:1=Generated using FastTree
+C:2=Created on %DateTime%
+C:3=PARAM:lr=0.25 ps=3 nl=5 mil=5 iter=20
+C:4=FG:I1:marital-status.Married-civ-spouse:10.5224365222305
+C:5=FG:I2:occupation.Prof-specialty:2.63037873952709
+C:6=FG:I3:occupation.Exec-managerial:1.99051710044467
+C:7=FG:I11:marital-status.Never-married:0.751743449126202
+C:8=FG:I7:education.Doctorate:0.685325991471107
+C:9=FG:I5:Workclass.Self-emp-inc:0.504269771979131
+C:10=FG:I9:ethnicity.Asian-Pac-Islander:0.412550907607706
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+C:24=FG:I28:Workclass.Self-emp-not-inc:0.0997611767187343
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+C:30=FG:I21:marital-status.Widowed:0.0423529410997556
+C:31=FG:I26:Workclass.State-gov:0.0301574677788823
+
+
+[Evaluator:22]
+EvaluatorType=Aggregator
+Type=Sigmoid
+Bias=0
+NumNodes=1
+Nodes=E:21
+Weights=0.5
+
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat.txt
new file mode 100644
index 0000000000..f0e217c24f
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census-Cat-Only.Cat.txt
@@ -0,0 +1,501 @@
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat-out.txt
new file mode 100644
index 0000000000..cef126d68c
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat-out.txt
@@ -0,0 +1,61 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat}
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise
+Processed 500 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 75 | 40 | 0.6522
+ negative || 6 | 379 | 0.9844
+ ||======================
+Precision || 0.9259 | 0.9045 |
+OVERALL 0/1 ACCURACY: 0.908000
+LOG LOSS/instance: 0.353688
+Test-set entropy (prior Log-Loss/instance): 0.778011
+LOG-LOSS REDUCTION (RIG): 0.545395
+AUC: 0.958893
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.958893 (0.0000)
+Accuracy: 0.908000 (0.0000)
+Positive precision: 0.925926 (0.0000)
+Positive recall: 0.652174 (0.0000)
+Negative precision: 0.904535 (0.0000)
+Negative recall: 0.984416 (0.0000)
+Log-loss: 0.353688 (0.0000)
+Log-loss reduction: 0.545395 (0.0000)
+F1 Score: 0.765306 (0.0000)
+AUPRC: 0.895540 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'Building term dictionary' started.
+[1] (%Time%) 500 examples Total Terms: 76
+[1] 'Building term dictionary' finished in %Time%.
+[2] 'FastTree data preparation' started.
+[2] 'FastTree data preparation' finished in %Time%.
+[3] 'FastTree in-memory bins initialization' started.
+[3] 'FastTree in-memory bins initialization' finished in %Time%.
+[4] 'FastTree feature conversion' started.
+[4] 'FastTree feature conversion' finished in %Time%.
+[5] 'FastTree data preparation #2' started.
+[5] 'FastTree data preparation #2' finished in %Time%.
+[6] 'FastTree feature conversion #2' started.
+[6] 'FastTree feature conversion #2' finished in %Time%.
+[7] 'FastTree training' started.
+[7] 'FastTree training' finished in %Time%.
+[8] 'Saving model' started.
+[8] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat-rp.txt
new file mode 100644
index 0000000000..0762e162a4
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat-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 /nl /mil /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.958893 0.908 0.925926 0.652174 0.904535 0.984416 0.353688 0.545395 0.765306 0.89554 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} dout=%Output% loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} /lr:0.25;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat-summary.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat-summary.txt
new file mode 100644
index 0000000000..ddf1cabfbd
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat-summary.txt
@@ -0,0 +1,22 @@
+
+Per-feature gain summary for the boosted tree ensemble:
+ Cat.marital-status.Married-civ-spouse 1
+ Num.capital-gain 0.78247640388701
+ Num.fnlwgt 0.545416657387531
+ Num.education-num 0.531707897890713
+ Num.age 0.409622531262292
+ Cat.occupation.Prof-specialty 0.357582620367965
+ Num.capital-loss 0.327970655093426
+ Num.hours-per-week 0.307112374569836
+ Cat.occupation.Exec-managerial 0.168470795393402
+ Cat.ethnicity.White 0.12766472086777
+ Cat.education.Masters 0.124640495264293
+ Cat.relationship.Husband 0.112403865114159
+ Cat.marital-status.Never-married 0.110824550796253
+ Cat.occupation.Protective-serv 0.0989436478612388
+ Cat.education.Bachelors 0.0987203691811107
+ Cat.native-country-region.Mexico 0.0950276929435107
+ Cat.occupation.Other-service 0.0883199693237364
+ Cat.occupation.Farming-fishing 0.0758713488112266
+ Cat.ethnicity.Asian-Pac-Islander 0.0738301375676183
+ Cat.occupation.Transport-moving 0.0622812764538133
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat.ini b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat.ini
new file mode 100644
index 0000000000..b7953d5491
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat.ini
@@ -0,0 +1,388 @@
+[TreeEnsemble]
+Inputs=20
+Evaluators=22
+
+[Input:1]
+Name=Cat.marital-status.Married-civ-spouse
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:2]
+Name=Num.capital-gain
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:3]
+Name=Num.education-num
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:4]
+Name=Cat.occupation.Prof-specialty
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:5]
+Name=Num.capital-loss
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:6]
+Name=Num.fnlwgt
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:7]
+Name=Num.age
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:8]
+Name=Num.hours-per-week
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:9]
+Name=Cat.occupation.Exec-managerial
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:10]
+Name=Cat.relationship.Husband
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:11]
+Name=Cat.education.Masters
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:12]
+Name=Cat.marital-status.Never-married
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:13]
+Name=Cat.occupation.Protective-serv
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:14]
+Name=Cat.ethnicity.White
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:15]
+Name=Cat.native-country-region.Mexico
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:16]
+Name=Cat.occupation.Other-service
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:17]
+Name=Cat.ethnicity.Asian-Pac-Islander
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:18]
+Name=Cat.education.Bachelors
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:19]
+Name=Cat.occupation.Farming-fishing
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:20]
+Name=Cat.occupation.Transport-moving
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Evaluator:1]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:3 I:2
+SplitGain=4.7088298291299 1.82393817313143 1.15134889109566 0.944729648284472
+GainPValue=5.502e-18 7.752e-08 2.025e-05 1.194e-04
+LTEChild=3 2 -2 -1
+GTChild=1 -3 -4 -5
+Threshold=0.5 5095.5 10.5 4792
+Output=-0.938697318007663 -0.421052631578947 1 0.233333333333333 0.666666666666667
+
+[Evaluator:2]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:4 I:2
+SplitGain=2.68797002190992 1.03782125239082 0.702469684950127 0.536490605065452
+GainPValue=5.720e-14 2.027e-05 5.830e-04 2.490e-04
+LTEChild=3 2 -2 -1
+GTChild=1 -3 -4 -5
+Threshold=0.5 5095.5 0.5 4792
+Output=-0.747963566988115 -0.271418660901274 0.803265329856317 0.402838788558462 0.515587010648571
+
+[Evaluator:3]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:5 I:6
+SplitGain=1.57896711216689 0.602950601535339 0.507933784729022 0.460067406857802
+GainPValue=1.986e-10 8.023e-04 2.839e-03 4.387e-03
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 5095.5 1881.5 207142.5
+Output=-0.616452312281396 -0.325227847195953 0.702953204648243 0.715005789478837 0.0879071517784774
+
+[Evaluator:4]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:3 I:6 I:7
+SplitGain=0.933961437525034 0.469845253691399 0.368778829041866 0.361434179229943
+GainPValue=1.498e-07 2.313e-03 6.995e-03 9.875e-03
+LTEChild=-1 2 -2 -4
+GTChild=1 -3 3 -5
+Threshold=0.5 10.5 115001 26.5
+Output=-0.540835682387761 -0.531147181613067 0.237930526342662 -0.85472434996025 0.00526136141356183
+
+[Evaluator:5]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:2 I:3 I:5 I:8
+SplitGain=0.728157533683411 0.553144315838373 0.406333943012827 0.320381360794014
+GainPValue=1.088e-06 2.448e-05 2.699e-04 1.127e-03
+LTEChild=1 2 3 -1
+GTChild=-2 -3 -4 -5
+Threshold=4792 15.5 1881.5 44.5
+Output=-0.416955329633573 0.584589466495732 1.07159032482301 0.545790389845197 -0.0736235783382999
+
+[Evaluator:6]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:7 I:6 I:8
+SplitGain=0.515353351089896 0.263607430449458 0.321439031330917 0.304206136711201
+GainPValue=1.898e-05 1.646e-02 8.649e-03 7.992e-03
+LTEChild=-1 -2 3 -3
+GTChild=1 2 -4 -5
+Threshold=0.5 26.5 207142.5 37.5
+Output=-0.453041322781326 -0.639214220190514 -0.551788762308723 0.274328102623981 0.0108302779342166
+
+[Evaluator:7]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:2 I:4 I:7 I:9
+SplitGain=0.429211498172382 0.37130253655033 0.245607013698752 0.240170720708446
+GainPValue=5.826e-05 2.090e-04 1.458e-02 2.066e-03
+LTEChild=1 3 -3 -1
+GTChild=-2 2 -4 -5
+Threshold=6897.5 0.5 38.5 0.5
+Output=-0.325018558560221 0.709504798114759 -0.0978229810975667 0.619123743179419 0.0738169429913811
+
+[Evaluator:8]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:7 I:8 I:6 I:3
+SplitGain=0.324507600154794 0.231880035343412 0.218742301145431 0.158753505887515
+GainPValue=3.333e-04 7.590e-03 1.751e-02 2.080e-02
+LTEChild=-1 3 -3 -2
+GTChild=1 2 -4 -5
+Threshold=26.5 40.5 343826.5 14.5
+Output=-0.538717070438754 -0.163437602533054 0.107912639659195 0.928282843335679 0.642680985019362
+
+[Evaluator:9]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:6 I:6 I:6
+SplitGain=0.263067945107473 0.221497887988851 0.33860104205235 0.320394503285078
+GainPValue=9.839e-04 2.092e-02 5.916e-03 6.456e-03
+LTEChild=-1 -2 -3 -4
+GTChild=1 2 3 -5
+Threshold=0.5 117215.5 135637 153300
+Output=-0.361179752457602 -0.197836735178024 1.09707453767085 -0.612339516540567 0.157332113494155
+
+[Evaluator:10]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:2 I:3 I:10 I:7
+SplitGain=0.237645949149002 0.171230238633491 0.136822585351253 0.187108623295558
+GainPValue=1.362e-03 7.096e-03 1.588e-02 3.971e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=4792 15.5 0.5 60
+Output=-0.339659136475996 0.537069257542093 0.862924154545784 0.0657893860383729 -0.427847068751094
+
+[Evaluator:11]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:4 I:6 I:6 I:11
+SplitGain=0.158028250346348 0.178485982354828 0.206838765522749 0.168233947018622
+GainPValue=8.034e-03 2.111e-02 1.419e-02 4.109e-02
+LTEChild=-1 3 -3 -2
+GTChild=1 2 -4 -5
+Threshold=0.5 100319 177610 0.5
+Output=-0.119308561404354 0.228243402552109 1.05719705454013 0.0598276043939003 -0.957602680739738
+
+[Evaluator:12]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:2 I:12 I:3 I:6
+SplitGain=0.146923659119402 0.13300478378088 0.141575583388046 0.115166085208399
+GainPValue=9.451e-03 1.462e-02 3.858e-02 7.112e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=7493 0.5 8.5 111675.5
+Output=-0.338640974417467 0.676917239927446 -0.464078313783936 -0.155141823898237 0.134390638105366
+
+[Evaluator:13]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:7 I:8 I:13 I:14
+SplitGain=0.113882941049341 0.0947541242081512 0.106015932587327 0.103800129357575
+GainPValue=2.065e-02 6.702e-02 4.259e-02 7.898e-02
+LTEChild=-1 2 -2 -3
+GTChild=1 3 -4 -5
+Threshold=26.5 40.5 0.5 0.5
+Output=-0.476743492588366 -0.0788770608124479 -0.411073358570253 0.744504044683463 0.213969436609267
+
+[Evaluator:14]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:5 I:2 I:4 I:6
+SplitGain=0.0885736781849435 0.0906152230827666 0.0920787482727172 0.096674480488273
+GainPValue=3.903e-02 3.534e-02 3.652e-02 7.200e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=1881.5 7493 0.5 100319
+Output=-0.120315893237392 0.392880244098873 0.644392747165095 -0.315040034480276 0.360910725076097
+
+[Evaluator:15]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:15 I:6 I:6
+SplitGain=0.0763790011512721 0.0977902763944078 0.0945681888577769 0.177637452629376
+GainPValue=5.334e-02 1.012e-01 1.097e-01 2.116e-02
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 207142.5 189250
+Output=-0.237552799101888 0.0627032836526124 -0.672146337661497 0.209890429165221 -0.506493119136646
+
+[Evaluator:16]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:9 I:5 I:14 I:7
+SplitGain=0.0671873028053795 0.161995914993803 0.0726967789328359 0.0880669874047782
+GainPValue=6.741e-02 9.100e-03 5.356e-02 3.922e-02
+LTEChild=2 -2 -1 -4
+GTChild=1 -3 3 -5
+Threshold=0.5 1457 0.5 60
+Output=-0.426029039803156 0.0843279580735084 0.877114094236869 0.0206591289893379 -0.408935848550922
+
+[Evaluator:17]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:7 I:8 I:16 I:7
+SplitGain=0.0657325821949111 0.0701644214009786 0.0844720669896727 0.167086317488132
+GainPValue=6.738e-02 1.024e-01 8.596e-02 1.815e-02
+LTEChild=-1 -2 3 -3
+GTChild=1 2 -4 -5
+Threshold=26.5 31 0.5 27.5
+Output=-0.44018259816661 -0.323439227342382 1.35075902903175 -0.448762931500328 0.0782616670819337
+
+[Evaluator:18]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:3 I:17 I:4 I:18
+SplitGain=0.0583015498348141 0.059028623710292 0.0607965965868098 0.105537996112979
+GainPValue=8.120e-02 7.980e-02 7.563e-02 4.257e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=15.5 0.5 0.5 0.5
+Output=-0.0475499334266732 0.758934721534646 -0.579100267851108 -0.0207753186547962 0.613424150388954
+
+[Evaluator:19]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:2 I:19 I:1 I:6
+SplitGain=0.0518864329133906 0.0623377197096143 0.0646445617187989 0.102563250702328
+GainPValue=9.681e-02 7.325e-02 6.841e-02 9.478e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=6897.5 0.5 0.5 153300
+Output=-0.239443828579011 0.635670391470562 -0.491899269148587 -0.0902190626578666 0.15193315669661
+
+[Evaluator:20]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:20 I:3 I:3 I:3
+SplitGain=0.0420059017164206 0.174259979330403 0.0933876246666109 0.0897042435901111
+GainPValue=1.325e-01 3.487e-02 8.920e-02 2.398e-02
+LTEChild=3 -2 -3 -1
+GTChild=1 2 -4 -5
+Threshold=0.5 8.5 9.5 8.5
+Output=-0.468182078145119 1.40393617893461 0.309735584528877 -0.719340829777538 0.0193254769476334
+
+[Evaluator:21]
+EvaluatorType=Aggregator
+NumNodes=20
+Nodes=E:1 E:2 E:3 E:4 E:5 E:6 E:7 E:8 E:9 E:10 E:11 E:12 E:13 E:14 E:15 E:16 E:17 E:18 E:19 E:20
+Weights=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+Bias=0
+Type=Linear
+
+[Comments]
+C:0=Regression Tree Ensemble
+C:1=Generated using FastTree
+C:2=Created on %DateTime%
+C:3=PARAM:lr=0.25 ps=3 nl=5 mil=5 iter=20
+C:4=FG:I1:Cat.marital-status.Married-civ-spouse:10.8291732597992
+C:5=FG:I2:Num.capital-gain:6.63037057652787
+C:6=FG:I6:Num.fnlwgt:3.22145520746603
+C:7=FG:I3:Num.education-num:3.06155118595643
+C:8=FG:I7:Num.age:1.81703367496567
+C:9=FG:I4:Cat.occupation.Prof-specialty:1.38467581670633
+C:10=FG:I5:Num.capital-loss:1.1648373209206
+C:11=FG:I8:Num.hours-per-week:1.02138607845776
+C:12=FG:I9:Cat.occupation.Exec-managerial:0.307358023513825
+C:13=FG:I14:Cat.ethnicity.White:0.176496908290411
+C:14=FG:I11:Cat.education.Masters:0.168233947018622
+C:15=FG:I10:Cat.relationship.Husband:0.136822585351253
+C:16=FG:I12:Cat.marital-status.Never-married:0.13300478378088
+C:17=FG:I13:Cat.occupation.Protective-serv:0.106015932587327
+C:18=FG:I18:Cat.education.Bachelors:0.105537996112979
+C:19=FG:I15:Cat.native-country-region.Mexico:0.0977902763944078
+C:20=FG:I16:Cat.occupation.Other-service:0.0844720669896727
+C:21=FG:I19:Cat.occupation.Farming-fishing:0.0623377197096143
+C:22=FG:I17:Cat.ethnicity.Asian-Pac-Islander:0.059028623710292
+C:23=FG:I20:Cat.occupation.Transport-moving:0.0420059017164206
+
+
+[Evaluator:22]
+EvaluatorType=Aggregator
+Type=Sigmoid
+Bias=0
+NumNodes=1
+Nodes=E:21
+Weights=0.5
+
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat.txt
new file mode 100644
index 0000000000..cc6f11880d
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-Census.Cat.txt
@@ -0,0 +1,501 @@
+Instance Label Score Probability Log-loss Assigned
+0 0 -8.317906 0.0153835537 0.022366257396670378 0
+1 0 -2.50008941 0.2226924 0.36344247326344925 0
+2 1 0.951580644 0.61675334 0.69723447203460132 1
+3 1 5.79287434 0.947670043 0.077543262440129226 1
+4 0 -7.383711 0.02431953 0.035519343768971659 0
+5 0 -6.981329 0.0295790248 0.043317360850078777 0
+6 0 -6.538809 0.0366358422 0.053846845748668527 0
+7 1 1.04070067 0.6272297 0.67293424238902255 1
+8 0 -7.383711 0.02431953 0.035519343768971659 0
+9 0 -4.64126444 0.08942857 0.13515589496868566 0
+10 1 2.9844718 0.816413641 0.29262780717137343 1
+11 0 0.1281995 0.516019464 1.0469790649825117 1
+12 0 -7.383711 0.02431953 0.035519343768971659 0
+13 0 -0.758855641 0.4062649 0.75210869388462442 0
+14 1 0.148672864 0.518575549 0.94737391026719076 1
+15 1 2.31860256 0.761205733 0.3936416679531356 1
+16 0 -7.383711 0.02431953 0.035519343768971659 0
+17 0 -2.25485373 0.244636282 0.40475660630010701 0
+18 0 -6.184885 0.0434200726 0.064042576414026761 0
+19 1 2.77338433 0.80006367 0.32181327953892991 1
+20 1 -0.6749455 0.4164235 1.2638766098788292 0
+21 0 -7.06583357 0.02839002 0.041550786230963248 0
+22 0 -7.2854867 0.025512496 0.037284409195350386 0
+23 0 -5.83517838 0.0512908846 0.075962285954297257 0
+24 0 -0.983661652 0.379462361 0.68840937531754109 0
+25 1 6.727579 0.9665535 0.049078491398986075 1
+26 0 -7.383711 0.02431953 0.035519343768971659 0
+27 0 -6.88859844 0.0309393238 0.045341094394138505 0
+28 0 -3.56855679 0.143775642 0.2239392166242126 0
+29 0 -3.27288985 0.162949383 0.25661322920429602 0
+30 1 5.411654 0.9373696 0.093310110639904228 1
+31 0 -6.073687 0.0457888879 0.067619607794601883 0
+32 0 -7.040379 0.0287432037 0.04207530621993677 0
+33 0 -3.823974 0.12875779 0.19885424290952752 0
+34 0 -6.61227 0.0353613235 0.05193943980725222 0
+35 0 -3.07510352 0.176891461 0.28084541110431011 0
+36 1 2.35098934 0.7641368 0.38809717081091522 1
+37 0 -7.527887 0.02266642 0.033077034293935444 0
+38 0 -7.871218 0.0191595387 0.027909601019618546 0
+39 0 -7.383711 0.02431953 0.035519343768971659 0
+40 1 1.57975936 0.6878055 0.53992749748857094 1
+41 1 0.9094982 0.611767769 0.70894399474335601 1
+42 0 -1.402688 0.3315143 0.58103139350011968 0
+43 0 -4.203969 0.108904086 0.16634736894505039 0
+44 0 -7.636166 0.0214975774 0.031352672214021708 0
+45 1 1.3113414 0.6582872 0.60321088968857328 1
+46 0 -2.60143161 0.214044571 0.3474805942006774 0
+47 0 -1.41381335 0.330282718 0.57837589760636021 0
+48 0 -7.30258 0.0253008734 0.036971143320342549 0
+49 0 -4.123619 0.112864524 0.17277365727536942 0
+50 0 -7.383711 0.02431953 0.035519343768971659 0
+51 0 -5.94034 0.0487918332 0.072166993218776898 0
+52 0 -7.383711 0.02431953 0.035519343768971659 0
+53 1 1.1788187 0.6432296 0.63659428876607316 1
+54 0 -5.084158 0.07296044 0.10929718422198825 0
+55 0 -7.383711 0.02431953 0.035519343768971659 0
+56 0 -3.517731 0.146932483 0.22926816460867439 0
+57 1 2.31860256 0.761205733 0.3936416679531356 1
+58 1 4.440768 0.9020651 0.14869654435012786 1
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-group-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-group-out.txt
new file mode 100644
index 0000000000..4321d0b961
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-group-out.txt
@@ -0,0 +1,62 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{col=Label:0 col=GroupId:U4[0-10]:1 col=Features:1-*} data=%Data% out=%Output% seed=1
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise
+Warning: This is not ranking problem, Group Id 'GroupId' column will be ignored
+Warning: Skipped 16 instances with missing features during training
+Processed 683 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise
+Warning: This is not ranking problem, Group Id 'GroupId' column will be ignored
+Warning: Skipped 16 instances with missing features during training
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 240 | 1 | 0.9959
+ negative || 13 | 445 | 0.9716
+ ||======================
+Precision || 0.9486 | 0.9978 |
+OVERALL 0/1 ACCURACY: 0.979971
+LOG LOSS/instance: 0.092572
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 0.900387
+AUC: 0.995370
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.995370 (0.0000)
+Accuracy: 0.979971 (0.0000)
+Positive precision: 0.948617 (0.0000)
+Positive recall: 0.995851 (0.0000)
+Negative precision: 0.997758 (0.0000)
+Negative recall: 0.971616 (0.0000)
+Log-loss: 0.092572 (0.0000)
+Log-loss reduction: 0.900387 (0.0000)
+F1 Score: 0.971660 (0.0000)
+AUPRC: 0.970606 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %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] 'FastTree data preparation #2' started.
+[4] 'FastTree data preparation #2' finished in %Time%.
+[5] 'FastTree feature conversion #2' started.
+[5] 'FastTree feature conversion #2' finished in %Time%.
+[6] 'FastTree training' started.
+[6] 'FastTree training' finished in %Time%.
+[7] 'Saving model' started.
+[7] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-group-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-group-rp.txt
new file mode 100644
index 0000000000..aedf77a9d1
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-group-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 /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 0.900387 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} dout=%Output% loader=Text{col=Label:0 col=GroupId:U4[0-10]:1 col=Features:1-*} data=%Data% out=%Output% seed=1 /lr:0.25;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-group.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-group.txt
new file mode 100644
index 0000000000..3e8aca5beb
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-group.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..9d72c71f9c
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,60 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} 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
+Warning: Skipped 16 instances with missing features during training
+Processed 683 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise
+Warning: Skipped 16 instances with missing features during training
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 240 | 1 | 0.9959
+ negative || 13 | 445 | 0.9716
+ ||======================
+Precision || 0.9486 | 0.9978 |
+OVERALL 0/1 ACCURACY: 0.979971
+LOG LOSS/instance: 0.092572
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 0.900387
+AUC: 0.995370
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.995370 (0.0000)
+Accuracy: 0.979971 (0.0000)
+Positive precision: 0.948617 (0.0000)
+Positive recall: 0.995851 (0.0000)
+Negative precision: 0.997758 (0.0000)
+Negative recall: 0.971616 (0.0000)
+Log-loss: 0.092572 (0.0000)
+Log-loss reduction: 0.900387 (0.0000)
+F1 Score: 0.971660 (0.0000)
+AUPRC: 0.970606 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %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] 'FastTree data preparation #2' started.
+[4] 'FastTree data preparation #2' finished in %Time%.
+[5] 'FastTree feature conversion #2' started.
+[5] 'FastTree feature conversion #2' finished in %Time%.
+[6] 'FastTree training' started.
+[6] 'FastTree training' finished in %Time%.
+[7] 'Saving model' started.
+[7] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..cb8f279f8f
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-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 /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 0.900387 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} 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;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..3e8aca5beb
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTree-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeBsr-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeBsr-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..8134c307a4
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeBsr-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,60 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 bsr+ lr=0.25 iter=20 mb=255} 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
+Warning: Skipped 16 instances with missing features during training
+Processed 683 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise
+Warning: Skipped 16 instances with missing features during training
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 233 | 8 | 0.9668
+ negative || 28 | 430 | 0.9389
+ ||======================
+Precision || 0.8927 | 0.9817 |
+OVERALL 0/1 ACCURACY: 0.948498
+LOG LOSS/instance: 0.837162
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 0.099166
+AUC: 0.983973
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.983973 (0.0000)
+Accuracy: 0.948498 (0.0000)
+Positive precision: 0.892720 (0.0000)
+Positive recall: 0.966805 (0.0000)
+Negative precision: 0.981735 (0.0000)
+Negative recall: 0.938865 (0.0000)
+Log-loss: 0.837162 (0.0000)
+Log-loss reduction: 0.099166 (0.0000)
+F1 Score: 0.928287 (0.0000)
+AUPRC: 0.939241 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %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] 'FastTree data preparation #2' started.
+[4] 'FastTree data preparation #2' finished in %Time%.
+[5] 'FastTree feature conversion #2' started.
+[5] 'FastTree feature conversion #2' finished in %Time%.
+[6] 'FastTree training' started.
+[6] 'FastTree training' finished in %Time%.
+[7] 'Saving model' started.
+[7] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeBsr-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeBsr-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..e45481cfa9
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeBsr-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 /bsr /lr /nl /mil /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.983973 0.948498 0.89272 0.966805 0.981735 0.938865 0.837162 0.099166 0.928287 0.939241 + 0.25 5 5 20 FastTreeBinaryClassification %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 bsr+ lr=0.25 iter=20 mb=255} 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 /bsr:+;/lr:0.25;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeBsr-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeBsr-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..e6bdb4d1ba
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeBsr-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-out.txt
new file mode 100644
index 0000000000..de576aac0d
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-out.txt
@@ -0,0 +1,61 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise
+Processed 500 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 67 | 48 | 0.5826
+ negative || 18 | 367 | 0.9532
+ ||======================
+Precision || 0.7882 | 0.8843 |
+OVERALL 0/1 ACCURACY: 0.868000
+LOG LOSS/instance: 0.439868
+Test-set entropy (prior Log-Loss/instance): 0.778011
+LOG-LOSS REDUCTION (RIG): 0.434625
+AUC: 0.916804
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.916804 (0.0000)
+Accuracy: 0.868000 (0.0000)
+Positive precision: 0.788235 (0.0000)
+Positive recall: 0.582609 (0.0000)
+Negative precision: 0.884337 (0.0000)
+Negative recall: 0.953247 (0.0000)
+Log-loss: 0.439868 (0.0000)
+Log-loss reduction: 0.434625 (0.0000)
+F1 Score: 0.670000 (0.0000)
+AUPRC: 0.770221 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'Building term dictionary' started.
+[1] (%Time%) 500 examples Total Terms: 76
+[1] 'Building term dictionary' finished in %Time%.
+[2] 'FastTree data preparation' started.
+[2] 'FastTree data preparation' finished in %Time%.
+[3] 'FastTree in-memory bins initialization' started.
+[3] 'FastTree in-memory bins initialization' finished in %Time%.
+[4] 'FastTree feature conversion' started.
+[4] 'FastTree feature conversion' finished in %Time%.
+[5] 'FastTree data preparation #2' started.
+[5] 'FastTree data preparation #2' finished in %Time%.
+[6] 'FastTree feature conversion #2' started.
+[6] 'FastTree feature conversion #2' finished in %Time%.
+[7] 'FastTree training' started.
+[7] 'FastTree training' finished in %Time%.
+[8] 'Saving model' started.
+[8] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-rp.txt
new file mode 100644
index 0000000000..cd7e6fe7bf
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-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 /cat /nl /mil /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.916804 0.868 0.788235 0.582609 0.884337 0.953247 0.439868 0.434625 0.67 0.770221 0.25 + 5 5 20 FastTreeBinaryClassification %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat} /lr:0.25;/cat:+;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt
new file mode 100644
index 0000000000..3db5bda64e
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt
@@ -0,0 +1,15 @@
+
+Per-feature gain summary for the boosted tree ensemble:
+ relationship.Own-child 1
+ occupation.Machine-op-inspct 0.827682056669332
+ education.11th 0.551730549184892
+ marital-status.Never-married 0.487965393166092
+ Workclass.Private 0.329794846863694
+ ethnicity.Black 0.241347181393574
+ education.Doctorate 0.127375328946519
+ occupation.Craft-repair 0.113440651301201
+ occupation.Transport-moving 0.079068634569739
+ relationship.Unmarried 0.0670380166293443
+ education.HS-grad 0.0665176693937191
+ Workclass.Self-emp-not-inc 0.0642015127924024
+ relationship.Husband 0.0551500385166614
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini
new file mode 100644
index 0000000000..e3c58db735
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini
@@ -0,0 +1,1143 @@
+[TreeEnsemble]
+Inputs=42
+Evaluators=92
+
+[Input:1]
+Name=relationship.Husband
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:2]
+Name=relationship.Wife
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:3]
+Name=occupation.Protective-serv
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:4]
+Name=occupation.Prof-specialty
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:5]
+Name=occupation.Exec-managerial
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:6]
+Name=occupation.Tech-support
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:7]
+Name=occupation.Transport-moving
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:8]
+Name=education.HS-grad
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:9]
+Name=education.Assoc-acdm
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:10]
+Name=education.Some-college
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:11]
+Name=education.Bachelors
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:12]
+Name=education.Masters
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:13]
+Name=education.Assoc-voc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:14]
+Name=education.9th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:15]
+Name=Workclass.Federal-gov
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:16]
+Name=Workclass.Self-emp-inc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:17]
+Name=occupation.Machine-op-inspct
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:18]
+Name=occupation.Craft-repair
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:19]
+Name=occupation.Adm-clerical
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:20]
+Name=occupation.Sales
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:21]
+Name=occupation.Handlers-cleaners
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:22]
+Name=education.Doctorate
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:23]
+Name=marital-status.Married-civ-spouse
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:24]
+Name=occupation.Farming-fishing
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:25]
+Name=marital-status.Divorced
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:26]
+Name=marital-status.Separated
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:27]
+Name=ethnicity.Black
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:28]
+Name=education.Prof-school
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:29]
+Name=ethnicity.White
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:30]
+Name=Workclass.Private
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:31]
+Name=Workclass.?
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:32]
+Name=education.10th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:33]
+Name=occupation.?
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:34]
+Name=Workclass.Self-emp-not-inc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:35]
+Name=relationship.Not-in-family
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:36]
+Name=relationship.Other-relative
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:37]
+Name=relationship.Unmarried
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:38]
+Name=education.11th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:39]
+Name=education.7th-8th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:40]
+Name=Workclass.State-gov
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:41]
+Name=occupation.Other-service
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:42]
+Name=Workclass.Local-gov
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Evaluator:1]
+EvaluatorType=DecisionTree
+NumInternalNodes=2
+SplitFeatures=I:1 I:2
+LTEChild=1 2
+GTChild=-1 -2
+Threshold=0.5 0.5
+Output=1 1 0
+
+[Evaluator:2]
+EvaluatorType=DecisionTree
+NumInternalNodes=5
+SplitFeatures=I:3 I:4 I:5 I:6 I:7
+LTEChild=1 2 3 4 5
+GTChild=-1 -2 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5 0.5
+Output=1 1 1 1 1 0
+
+[Evaluator:3]
+EvaluatorType=DecisionTree
+NumInternalNodes=7
+SplitFeatures=I:8 I:9 I:10 I:11 I:12 I:13 I:14
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+GTChild=-1 -2 -3 -4 -5 -6 -7
+Threshold=0.5 0.5 0.5 0.5 0.5 0.5 0.5
+Output=1 1 1 1 1 1 1 0
+
+[Evaluator:4]
+EvaluatorType=DecisionTree
+NumInternalNodes=2
+SplitFeatures=I:15 I:16
+LTEChild=1 2
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+Threshold=0.5 0.5
+Output=1 1 0
+
+[Evaluator:5]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=E:1 E:2 E:3 E:4
+SplitGain=4.90148266166068 2.33354921400773 0.356034913887413 0.23831980652566
+GainPValue=1.171e-18 1.222e-09 1.869e-02 5.468e-02
+LTEChild=-1 2 -2 -4
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+Threshold=0.5 0.5 0.5 0.5
+Output=-0.904059040590406 -0.91304347826087 0.353535353535354 -0.431578947368421 0.166666666666667
+
+[Evaluator:6]
+EvaluatorType=DecisionTree
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+LTEChild=1 2
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+
+[Evaluator:7]
+EvaluatorType=DecisionTree
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+LTEChild=1 2 3 4
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+Threshold=0.5 0.5 0.5 0.5
+Output=1 1 1 1 0
+
+[Evaluator:8]
+EvaluatorType=DecisionTree
+NumInternalNodes=3
+SplitFeatures=I:9 I:12 I:14
+LTEChild=1 2 3
+GTChild=-1 -2 -3
+Threshold=0.5 0.5 0.5
+Output=1 1 1 0
+
+[Evaluator:9]
+EvaluatorType=DecisionTree
+NumInternalNodes=6
+SplitFeatures=I:17 I:18 I:19 I:20 I:7 I:21
+LTEChild=1 2 3 4 5 6
+GTChild=-1 -2 -3 -4 -5 -6
+Threshold=0.5 0.5 0.5 0.5 0.5 0.5
+Output=1 1 1 1 1 1 0
+
+[Evaluator:10]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=E:6 E:7 E:8 E:9
+SplitGain=2.78777474894519 1.40721749228691 0.296530428644621 0.255759707993547
+GainPValue=2.989e-14 7.080e-07 2.253e-02 3.389e-02
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.7172417911995 -0.688238756258056 0.336144581428692 0.644217111475452 -0.266380482323226
+
+[Evaluator:11]
+EvaluatorType=DecisionTree
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+SplitFeatures=I:1 I:2
+LTEChild=1 2
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+Threshold=0.5 0.5
+Output=1 1 0
+
+[Evaluator:12]
+EvaluatorType=DecisionTree
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+Output=1 1 1 1 0
+
+[Evaluator:13]
+EvaluatorType=DecisionTree
+NumInternalNodes=3
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+GTChild=-1 -2 -3
+Threshold=0.5 0.5 0.5
+Output=1 1 1 0
+
+[Evaluator:14]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=E:11 E:12 E:13 I:22
+SplitGain=1.6213634184004 0.798877595499775 0.167641820015544 0.159797083069118
+GainPValue=1.765e-10 1.083e-04 7.381e-02 9.090e-02
+LTEChild=-1 2 -2 -3
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+Threshold=0.5 0.5 0.5 0.5
+Output=-0.609699147491391 -0.278168913618011 0.203625112369354 0.489857387517128 0.854166850411819
+
+[Evaluator:15]
+EvaluatorType=DecisionTree
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+[Evaluator:16]
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+Output=1 1 1 0
+
+[Evaluator:17]
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+Output=1 1 1 1 1 1 0
+
+[Evaluator:18]
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+Output=1 1 1 0
+
+[Evaluator:19]
+EvaluatorType=DecisionTree
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+SplitFeatures=E:15 E:16 E:17 E:18
+SplitGain=0.957953859718505 0.414124908886716 0.223893363128151 0.173995107460813
+GainPValue=1.672e-07 4.339e-03 3.422e-02 4.512e-03
+LTEChild=3 2 -2 -1
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+Threshold=0.5 0.5 0.5 0.5
+Output=-0.612600725063679 -0.589902474958851 0.171877388183413 -0.114993765565278 -0.285584617470486
+
+[Evaluator:20]
+EvaluatorType=DecisionTree
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+
+[Evaluator:21]
+EvaluatorType=DecisionTree
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+
+[Evaluator:22]
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+
+[Evaluator:23]
+EvaluatorType=DecisionTree
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+SplitGain=0.605570850822916 0.400756234920426 0.186704945484041 0.167648055071064
+GainPValue=1.384e-05 2.610e-03 5.715e-02 4.434e-02
+LTEChild=-1 3 -3 -2
+GTChild=1 2 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.552909177387328 -0.219871934332912 0.123464954362214 1.02224640821468 0.627891254692842
+
+[Evaluator:24]
+EvaluatorType=DecisionTree
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+
+[Evaluator:25]
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+
+[Evaluator:26]
+EvaluatorType=DecisionTree
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+
+[Evaluator:27]
+EvaluatorType=DecisionTree
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+GTChild=-1 -2 -3 -4 -5 -6
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+Output=1 1 1 1 1 1 0
+
+[Evaluator:28]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=E:24 E:25 E:26 E:27
+SplitGain=0.406119927291017 0.315074378337769 0.174593193154452 0.189259878373443
+GainPValue=2.391e-04 1.094e-02 6.173e-02 5.203e-02
+LTEChild=-1 2 -2 -4
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+Threshold=0.5 0.5 0.5 0.5
+Output=-0.434463646897514 -0.717906224875631 0.603034187141039 -0.234934643381507 0.0607167944379656
+
+[Evaluator:29]
+EvaluatorType=DecisionTree
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+
+[Evaluator:30]
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+
+[Evaluator:31]
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+Output=1 1 1 1 0
+
+[Evaluator:32]
+EvaluatorType=DecisionTree
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+LTEChild=1 2 3 4 5
+GTChild=-1 -2 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5 0.5
+Output=1 1 1 1 1 0
+
+[Evaluator:33]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=E:29 E:30 E:31 E:32
+SplitGain=0.262232067673786 0.254774505144943 0.206519256204453 0.248632434225749
+GainPValue=2.511e-03 1.370e-02 2.468e-02 1.224e-02
+LTEChild=-1 2 -2 -4
+GTChild=1 -3 3 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.476428796513903 -0.432801731193848 0.245575274272306 -0.199699759370061 0.170023850908848
+
+[Evaluator:34]
+EvaluatorType=DecisionTree
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+
+[Evaluator:35]
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+
+[Evaluator:36]
+EvaluatorType=DecisionTree
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+SplitFeatures=I:27 I:29
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+Output=1 1 0
+
+[Evaluator:37]
+EvaluatorType=DecisionTree
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+GTChild=-1 -2 -3 -4 -5 -6 -7
+Threshold=0.5 0.5 0.5 0.5 0.5 0.5 0.5
+Output=1 1 1 1 1 1 1 0
+
+[Evaluator:38]
+EvaluatorType=DecisionTree
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+SplitFeatures=E:34 E:35 E:36 E:37
+SplitGain=0.172449999612001 0.118887347541285 0.117480735429586 0.108976252723307
+GainPValue=1.243e-02 1.036e-01 1.115e-01 1.282e-01
+LTEChild=-1 2 -2 -4
+GTChild=1 -3 3 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.371034653316342 -0.59812941745348 0.470698064228083 -0.42779078804276 0.0207070856423553
+
+[Evaluator:39]
+EvaluatorType=DecisionTree
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+
+[Evaluator:40]
+EvaluatorType=DecisionTree
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+
+[Evaluator:41]
+EvaluatorType=DecisionTree
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+
+[Evaluator:42]
+EvaluatorType=DecisionTree
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+[Evaluator:89]
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+[Evaluator:90]
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+GainPValue=3.572e-01 3.444e-01 2.588e-01 3.290e-01
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+Output=-0.28131369213035 -0.542933189795858 -0.0934068176897635 -0.0105102942195104 0.0556317182199142
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+[Evaluator:91]
+EvaluatorType=Aggregator
+NumNodes=90
+Nodes=E:1 E:2 E:3 E:4 E:5 E:6 E:7 E:8 E:9 E:10 E:11 E:12 E:13 E:14 E:15 E:16 E:17 E:18 E:19 E:20
+Weights=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+Bias=0
+Type=Linear
+
+[Comments]
+C:0=Regression Tree Ensemble
+C:1=Generated using FastTree
+C:2=Created on %DateTime%
+C:3=PARAM:lr=0.25 ps=3 cat=+ nl=5 mil=5 iter=20
+C:4=FG:I0:relationship.Own-child:9.84913772186264
+C:5=FG:I17:occupation.Machine-op-inspct:6.74722652110385
+C:6=FG:I38:education.11th:2.99814251604794
+C:7=FG:I0:marital-status.Never-married:2.34518039829699
+C:8=FG:I30:Workclass.Private:1.07123792865373
+C:9=FG:I27:ethnicity.Black:0.573697123995946
+C:10=FG:I22:education.Doctorate:0.159797083069118
+C:11=FG:I18:occupation.Craft-repair:0.126746400002432
+C:12=FG:I7:occupation.Transport-moving:0.0615753215486336
+C:13=FG:I37:relationship.Unmarried:0.0442629672244764
+C:14=FG:I8:education.HS-grad:0.0435784981283443
+C:15=FG:I34:Workclass.Self-emp-not-inc:0.04059651314405
+C:16=FG:I1:relationship.Husband:0.0299564158296147
+
+
+[Evaluator:92]
+EvaluatorType=Aggregator
+Type=Sigmoid
+Bias=0
+NumNodes=1
+Nodes=E:91
+Weights=0.5
+
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt
new file mode 100644
index 0000000000..219fc02027
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt
@@ -0,0 +1,501 @@
+Instance Label Score Probability Log-loss Assigned
+0 0 -7.637625 0.0214822348 0.031330051390025281 0
+1 0 -2.46320653 0.225900933 0.36940988496152494 0
+2 1 4.08608961 0.885242939 0.17585466366117686 1
+3 1 -2.21279955 0.2485427 2.0084343865240335 0
+4 0 -7.750194 0.02033042 0.029632851103238187 0
+5 0 -7.37519 0.02442083 0.035669139256831732 0
+6 0 -7.38192 0.0243407879 0.035550777910280988 0
+7 1 1.96474159 0.7275784 0.45882537819917563 1
+8 0 -7.44971848 0.0235485863 0.034379834645606552 0
+9 0 -3.57230067 0.143545359 0.22355125427043021 0
+10 1 -1.87889564 0.281011879 1.8312969749291836 0
+11 0 -0.952873945 0.383093834 0.69687702857716516 0
+12 0 -7.11948442 0.0276593547 0.040466265476125106 0
+13 0 -2.55277944 0.2181654 0.35506465819743865 0
+14 1 -1.87889564 0.281011879 1.8312969749291836 0
+15 1 1.70492184 0.7010831 0.51234258785967901 1
+16 0 -7.861786 0.0192483682 0.028040264216844635 0
+17 0 -1.87889564 0.281011879 0.47596016088024029 0
+18 0 -7.38192 0.0243407879 0.035550777910280988 0
+19 1 4.42098665 0.901187837 0.15010025250741646 1
+20 1 0.5070524 0.56304425 0.82867978579070456 1
+21 0 -7.44971848 0.0235485863 0.034379834645606552 0
+22 0 -5.70947647 0.0544369072 0.080754369474963367 0
+23 0 -4.61170244 0.0906395242 0.13707579429227171 0
+24 0 2.85176563 0.806259 2.3677986262739763 1
+25 1 -1.754451 0.293753058 1.7673242259091793 0
+26 0 -7.38192 0.0243407879 0.035550777910280988 0
+27 0 -4.45876169 0.0971429348 0.14743048774397932 0
+28 0 -1.22813857 0.3511315 0.62400196166828825 0
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat-out.txt
new file mode 100644
index 0000000000..26cd4ba70e
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat-out.txt
@@ -0,0 +1,61 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat}
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise
+Processed 500 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 83 | 32 | 0.7217
+ negative || 11 | 374 | 0.9714
+ ||======================
+Precision || 0.8830 | 0.9212 |
+OVERALL 0/1 ACCURACY: 0.914000
+LOG LOSS/instance: 0.327460
+Test-set entropy (prior Log-Loss/instance): 0.778011
+LOG-LOSS REDUCTION (RIG): 0.579106
+AUC: 0.965037
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.965037 (0.0000)
+Accuracy: 0.914000 (0.0000)
+Positive precision: 0.882979 (0.0000)
+Positive recall: 0.721739 (0.0000)
+Negative precision: 0.921182 (0.0000)
+Negative recall: 0.971429 (0.0000)
+Log-loss: 0.327460 (0.0000)
+Log-loss reduction: 0.579106 (0.0000)
+F1 Score: 0.794258 (0.0000)
+AUPRC: 0.907541 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'Building term dictionary' started.
+[1] (%Time%) 500 examples Total Terms: 76
+[1] 'Building term dictionary' finished in %Time%.
+[2] 'FastTree data preparation' started.
+[2] 'FastTree data preparation' finished in %Time%.
+[3] 'FastTree in-memory bins initialization' started.
+[3] 'FastTree in-memory bins initialization' finished in %Time%.
+[4] 'FastTree feature conversion' started.
+[4] 'FastTree feature conversion' finished in %Time%.
+[5] 'FastTree data preparation #2' started.
+[5] 'FastTree data preparation #2' finished in %Time%.
+[6] 'FastTree feature conversion #2' started.
+[6] 'FastTree feature conversion #2' finished in %Time%.
+[7] 'FastTree training' started.
+[7] 'FastTree training' finished in %Time%.
+[8] 'Saving model' started.
+[8] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat-rp.txt
new file mode 100644
index 0000000000..83a86c7bee
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat-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 /cat /nl /mil /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.965037 0.914 0.882979 0.721739 0.921182 0.971429 0.32746 0.579106 0.794258 0.907541 0.25 + 5 5 20 FastTreeBinaryClassification %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=%Output% loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} /lr:0.25;/cat:+;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat-summary.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat-summary.txt
new file mode 100644
index 0000000000..acfcb78873
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat-summary.txt
@@ -0,0 +1,16 @@
+
+Per-feature gain summary for the boosted tree ensemble:
+ Cat.relationship.Own-child 1
+ Num.capital-gain 0.832448370265449
+ Cat.occupation.Machine-op-inspct 0.774714808528682
+ Num.fnlwgt 0.476323264718471
+ Num.age 0.453280196772707
+ Cat.education.11th 0.423014907185028
+ Cat.marital-status.Never-married 0.380640908665174
+ Num.capital-loss 0.290129874275856
+ Num.hours-per-week 0.224975332787427
+ Num.education-num 0.170449309712328
+ Cat.education.Bachelors 0.165329576733931
+ Cat.ethnicity.Black 0.141652260091366
+ Cat.education.Some-college 0.116273411695884
+ Cat.Workclass.Private 0.098035998522615
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat.ini b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat.ini
new file mode 100644
index 0000000000..7e71fa59ef
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat.ini
@@ -0,0 +1,766 @@
+[TreeEnsemble]
+Inputs=36
+Evaluators=54
+
+[Input:1]
+Name=Cat.relationship.Husband
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:2]
+Name=Cat.relationship.Wife
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:3]
+Name=Cat.occupation.Protective-serv
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:4]
+Name=Cat.occupation.Prof-specialty
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:5]
+Name=Cat.occupation.Exec-managerial
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:6]
+Name=Cat.occupation.Tech-support
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:7]
+Name=Cat.occupation.Transport-moving
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:8]
+Name=Num.capital-gain
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:9]
+Name=Cat.occupation.Sales
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:10]
+Name=Num.fnlwgt
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:11]
+Name=Cat.marital-status.Married-civ-spouse
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:12]
+Name=Cat.marital-status.Separated
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:13]
+Name=Cat.education.Assoc-acdm
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:14]
+Name=Cat.education.Prof-school
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:15]
+Name=Cat.education.Doctorate
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:16]
+Name=Num.capital-loss
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:17]
+Name=Cat.occupation.Handlers-cleaners
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:18]
+Name=Cat.education.10th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:19]
+Name=Cat.education.Assoc-voc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:20]
+Name=Num.age
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:21]
+Name=Cat.occupation.Craft-repair
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:22]
+Name=Cat.occupation.Adm-clerical
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:23]
+Name=Num.hours-per-week
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:24]
+Name=Cat.education.Bachelors
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:25]
+Name=Cat.education.HS-grad
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:26]
+Name=Cat.education.Some-college
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:27]
+Name=Cat.education.Masters
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:28]
+Name=Cat.education.9th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:29]
+Name=Num.education-num
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:30]
+Name=Cat.marital-status.Divorced
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:31]
+Name=Cat.ethnicity.Black
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:32]
+Name=Cat.ethnicity.White
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:33]
+Name=Cat.Workclass.Private
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:34]
+Name=Cat.Workclass.?
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:35]
+Name=Cat.Workclass.Federal-gov
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:36]
+Name=Cat.Workclass.Self-emp-inc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Evaluator:1]
+EvaluatorType=DecisionTree
+NumInternalNodes=2
+SplitFeatures=I:1 I:2
+LTEChild=1 2
+GTChild=-1 -2
+Threshold=0.5 0.5
+Output=1 1 0
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+[Evaluator:49]
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+[Evaluator:50]
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+[Evaluator:52]
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+[Evaluator:53]
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+NumNodes=52
+Nodes=E:1 E:2 E:3 E:4 E:5 E:6 E:7 E:8 E:9 E:10 E:11 E:12 E:13 E:14 E:15 E:16 E:17 E:18 E:19 E:20
+Weights=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+Bias=0
+Type=Linear
+
+[Comments]
+C:0=Regression Tree Ensemble
+C:1=Generated using FastTree
+C:2=Created on %DateTime%
+C:3=PARAM:lr=0.25 ps=3 cat=+ nl=5 mil=5 iter=20
+C:4=FG:I0:Cat.relationship.Own-child:9.90015795158723
+C:5=FG:I8:Num.capital-gain:6.86051531841734
+C:6=FG:I0:Cat.occupation.Machine-op-inspct:5.9419068419439
+C:7=FG:I10:Num.fnlwgt:2.24618597653404
+C:8=FG:I20:Num.age:2.03411552738139
+C:9=FG:I0:Cat.education.11th:1.7715502199491
+C:10=FG:I0:Cat.marital-status.Never-married:1.43440914857036
+C:11=FG:I16:Num.capital-loss:0.833349200707693
+C:12=FG:I23:Num.hours-per-week:0.501085608137751
+C:13=FG:I29:Num.education-num:0.287628964058231
+C:14=FG:I24:Cat.education.Bachelors:0.270609619963889
+C:15=FG:I31:Cat.ethnicity.Black:0.198650260966922
+C:16=FG:I26:Cat.education.Some-college:0.13384524747474
+C:17=FG:I33:Cat.Workclass.Private:0.0951509824443383
+
+
+[Evaluator:54]
+EvaluatorType=Aggregator
+Type=Sigmoid
+Bias=0
+NumNodes=1
+Nodes=E:53
+Weights=0.5
+
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat.txt
new file mode 100644
index 0000000000..277b52433e
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategorical-TrainTest-Census.Cat.txt
@@ -0,0 +1,501 @@
+Instance Label Score Probability Log-loss Assigned
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-out.txt
new file mode 100644
index 0000000000..c02a57d406
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-out.txt
@@ -0,0 +1,55 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=%Output% loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise on disk
+Processed 500 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise on disk
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 68 | 47 | 0.5913
+ negative || 16 | 369 | 0.9584
+ ||======================
+Precision || 0.8095 | 0.8870 |
+OVERALL 0/1 ACCURACY: 0.874000
+LOG LOSS/instance: 0.425411
+Test-set entropy (prior Log-Loss/instance): 0.778011
+LOG-LOSS REDUCTION (RIG): 0.453207
+AUC: 0.923817
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.923817 (0.0000)
+Accuracy: 0.874000 (0.0000)
+Positive precision: 0.809524 (0.0000)
+Positive recall: 0.591304 (0.0000)
+Negative precision: 0.887019 (0.0000)
+Negative recall: 0.958442 (0.0000)
+Log-loss: 0.425411 (0.0000)
+Log-loss reduction: 0.453207 (0.0000)
+F1 Score: 0.683417 (0.0000)
+AUPRC: 0.792176 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'Building term dictionary' started.
+[1] (%Time%) 500 examples Total Terms: 76
+[1] 'Building term dictionary' finished in %Time%.
+[2] 'FastTree disk-based bins initialization' started.
+[2] 'FastTree disk-based bins initialization' finished in %Time%.
+[3] 'FastTree disk-based bins initialization #2' started.
+[3] 'FastTree disk-based bins initialization #2' finished in %Time%.
+[4] 'FastTree training' started.
+[4] 'FastTree training' finished in %Time%.
+[5] 'Saving model' started.
+[5] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-rp.txt
new file mode 100644
index 0000000000..6398e275b4
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-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 /cat /nl /mil /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.923817 0.874 0.809524 0.591304 0.887019 0.958442 0.425411 0.453207 0.683417 0.792176 0.25 + + 5 5 20 FastTreeBinaryClassification %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=%Output% loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat} /lr:0.25;/dt:+;/cat:+;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt
new file mode 100644
index 0000000000..fd18a8da91
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt
@@ -0,0 +1,14 @@
+
+Per-feature gain summary for the boosted tree ensemble:
+ relationship.Own-child 1
+ occupation.Machine-op-inspct 0.880278821582324
+ marital-status.Never-married 0.636189547653334
+ education.11th 0.531653264369975
+ Workclass.Private 0.385322535658439
+ native-country-region.United-States 0.33460937598556
+ ethnicity.Black 0.217587401712812
+ education.Doctorate 0.140045184209198
+ Workclass.Local-gov 0.127340271504787
+ Workclass.Self-emp-inc 0.0950288918046404
+ marital-status.Married-civ-spouse 0.0857004482164758
+ occupation.Prof-specialty 0.0645281020239454
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini
new file mode 100644
index 0000000000..3e200d4be6
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini
@@ -0,0 +1,1211 @@
+[TreeEnsemble]
+Inputs=52
+Evaluators=93
+
+[Input:1]
+Name=relationship.Husband
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:2]
+Name=relationship.Wife
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:3]
+Name=occupation.Protective-serv
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:4]
+Name=occupation.Prof-specialty
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:5]
+Name=occupation.Exec-managerial
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:6]
+Name=occupation.Tech-support
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:7]
+Name=occupation.Transport-moving
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:8]
+Name=education.HS-grad
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:9]
+Name=education.Assoc-acdm
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:10]
+Name=education.Some-college
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:11]
+Name=education.Bachelors
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:12]
+Name=education.Masters
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:13]
+Name=education.Assoc-voc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:14]
+Name=education.9th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:15]
+Name=Workclass.Federal-gov
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:16]
+Name=Workclass.Self-emp-inc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:17]
+Name=occupation.Machine-op-inspct
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:18]
+Name=occupation.Craft-repair
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:19]
+Name=occupation.Adm-clerical
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:20]
+Name=occupation.Sales
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:21]
+Name=occupation.Handlers-cleaners
+Transform=Linear
+Slope=1
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+[Evaluator:91]
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+Weights=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+Bias=0
+Type=Linear
+
+[Comments]
+C:0=Regression Tree Ensemble
+C:1=Generated using FastTree
+C:2=Created on %DateTime%
+C:3=PARAM:lr=0.25 ps=3 dt=+ cat=+ nl=5 mil=5 iter=20
+C:4=FG:I0:relationship.Own-child:8.71335286502078
+C:5=FG:I17:occupation.Machine-op-inspct:6.75189700472738
+C:6=FG:I50:marital-status.Never-married:3.52661752313375
+C:7=FG:I52:education.11th:2.46287444022911
+C:8=FG:I35:Workclass.Private:1.29370161745399
+C:9=FG:I24:native-country-region.United-States:0.975576912755885
+C:10=FG:I31:ethnicity.Black:0.412527394987369
+C:11=FG:I28:education.Doctorate:0.170891971612126
+C:12=FG:I32:Workclass.Local-gov:0.141291763278386
+C:13=FG:I16:Workclass.Self-emp-inc:0.0786858483330256
+C:14=FG:I22:marital-status.Married-civ-spouse:0.0639958023826359
+C:15=FG:I4:occupation.Prof-specialty:0.036281320445605
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+
+[Evaluator:93]
+EvaluatorType=Aggregator
+Type=Sigmoid
+Bias=0
+NumNodes=1
+Nodes=E:92
+Weights=0.5
+
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt
new file mode 100644
index 0000000000..3382d20a4c
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt
@@ -0,0 +1,501 @@
+Instance Label Score Probability Log-loss Assigned
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+3 1 -1.67593277 0.30196327 1.7275550215925228 0
+4 0 -7.98749876 0.0180969462 0.026347504845643268 0
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+7 1 2.21988344 0.75211823 0.41096862948141677 1
+8 0 -7.83602047 0.0194930788 0.028400280760623817 0
+9 0 -2.900505 0.1899627 0.30393975322026839 0
+10 1 -1.91349435 0.277529925 1.849284754137505 0
+11 0 -1.09438336 0.3665162 0.65862037502700266 0
+12 0 -6.70876169 0.033752 0.049534574746804838 0
+13 0 -2.48040152 0.224401042 0.36661722941968328 0
+14 1 -1.91349435 0.277529925 1.849284754137505 0
+15 1 1.44575679 0.673240542 0.57080603679454189 1
+16 0 -7.95984125 0.01834432 0.026711013707565963 0
+17 0 -1.91349435 0.277529925 0.46899026325875787 0
+18 0 -7.5347724 0.02259028 0.032964642509441852 0
+19 1 5.033063 0.9252927 0.11201833161242418 1
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+21 0 -7.83602047 0.0194930788 0.028400280760623817 0
+22 0 -5.20427227 0.06900107 0.10314858698621314 0
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat-out.txt
new file mode 100644
index 0000000000..f9871b0ee7
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat-out.txt
@@ -0,0 +1,55 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=%Output% loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat}
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise on disk
+Processed 500 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise on disk
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 84 | 31 | 0.7304
+ negative || 10 | 375 | 0.9740
+ ||======================
+Precision || 0.8936 | 0.9236 |
+OVERALL 0/1 ACCURACY: 0.918000
+LOG LOSS/instance: 0.322363
+Test-set entropy (prior Log-Loss/instance): 0.778011
+LOG-LOSS REDUCTION (RIG): 0.585658
+AUC: 0.965624
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.965624 (0.0000)
+Accuracy: 0.918000 (0.0000)
+Positive precision: 0.893617 (0.0000)
+Positive recall: 0.730435 (0.0000)
+Negative precision: 0.923645 (0.0000)
+Negative recall: 0.974026 (0.0000)
+Log-loss: 0.322363 (0.0000)
+Log-loss reduction: 0.585658 (0.0000)
+F1 Score: 0.803828 (0.0000)
+AUPRC: 0.910384 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'Building term dictionary' started.
+[1] (%Time%) 500 examples Total Terms: 76
+[1] 'Building term dictionary' finished in %Time%.
+[2] 'FastTree disk-based bins initialization' started.
+[2] 'FastTree disk-based bins initialization' finished in %Time%.
+[3] 'FastTree disk-based bins initialization #2' started.
+[3] 'FastTree disk-based bins initialization #2' finished in %Time%.
+[4] 'FastTree training' started.
+[4] 'FastTree training' finished in %Time%.
+[5] 'Saving model' started.
+[5] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat-rp.txt
new file mode 100644
index 0000000000..e77f73e292
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat-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 /cat /nl /mil /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.965624 0.918 0.893617 0.730435 0.923645 0.974026 0.322363 0.585658 0.803828 0.910384 0.25 + + 5 5 20 FastTreeBinaryClassification %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=%Output% loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} /lr:0.25;/dt:+;/cat:+;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt
new file mode 100644
index 0000000000..e608d56ea7
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt
@@ -0,0 +1,17 @@
+
+Per-feature gain summary for the boosted tree ensemble:
+ Cat.relationship.Own-child 1
+ Num.capital-gain 0.910977888533178
+ Cat.occupation.Machine-op-inspct 0.831716487240854
+ Cat.marital-status.Never-married 0.634181911132067
+ Num.fnlwgt 0.559553975672239
+ Num.age 0.480974220726686
+ Cat.education.11th 0.434434725983017
+ Num.capital-loss 0.30653202998531
+ Num.hours-per-week 0.28872978624719
+ Cat.native-country-region.United-States 0.222223818752412
+ Cat.education.Bachelors 0.172054656602638
+ Num.education-num 0.160288766325492
+ Cat.Workclass.Private 0.138584254245354
+ Cat.marital-status.Married-civ-spouse 0.115435335959736
+ Cat.ethnicity.Black 0.0836832837100329
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat.ini b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat.ini
new file mode 100644
index 0000000000..ef14b1b1d0
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat.ini
@@ -0,0 +1,848 @@
+[TreeEnsemble]
+Inputs=48
+Evaluators=55
+
+[Input:1]
+Name=Cat.relationship.Husband
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:2]
+Name=Cat.relationship.Wife
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:3]
+Name=Cat.occupation.Protective-serv
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:4]
+Name=Cat.occupation.Prof-specialty
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:5]
+Name=Cat.occupation.Exec-managerial
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:6]
+Name=Cat.occupation.Tech-support
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:7]
+Name=Cat.occupation.Transport-moving
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:8]
+Name=Num.capital-gain
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:9]
+Name=Cat.marital-status.Married-civ-spouse
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:10]
+Name=Cat.marital-status.Married-AF-spouse
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:11]
+Name=Cat.occupation.Sales
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:12]
+Name=Num.fnlwgt
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:13]
+Name=Cat.marital-status.Separated
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:14]
+Name=Cat.education.Assoc-acdm
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:15]
+Name=Cat.education.Prof-school
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:16]
+Name=Cat.education.Doctorate
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:17]
+Name=Num.capital-loss
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:18]
+Name=Cat.occupation.Adm-clerical
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:19]
+Name=Cat.occupation.Handlers-cleaners
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:20]
+Name=Num.age
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:21]
+Name=Cat.education.7th-8th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:22]
+Name=Cat.education.Bachelors
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:23]
+Name=Cat.education.Masters
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:24]
+Name=Cat.education.Assoc-voc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:25]
+Name=Cat.education.9th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:26]
+Name=Num.hours-per-week
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:27]
+Name=Cat.occupation.Craft-repair
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:28]
+Name=Cat.native-country-region.United-States
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:29]
+Name=Cat.native-country-region.?
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:30]
+Name=Cat.native-country-region.Ireland
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:31]
+Name=Cat.native-country-region.El-Salvador
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:32]
+Name=Cat.native-country-region.Japan
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:33]
+Name=Cat.native-country-region.Poland
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:34]
+Name=Cat.marital-status.Divorced
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:35]
+Name=Num.education-num
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:36]
+Name=Cat.education.HS-grad
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:37]
+Name=Cat.education.Some-college
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:38]
+Name=Cat.education.10th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:39]
+Name=Cat.Workclass.Private
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:40]
+Name=Cat.Workclass.?
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:41]
+Name=Cat.Workclass.Federal-gov
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:42]
+Name=Cat.Workclass.Self-emp-inc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:43]
+Name=Cat.ethnicity.Black
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:44]
+Name=Cat.ethnicity.White
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:45]
+Name=Cat.native-country-region.Guatemala
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:46]
+Name=Cat.native-country-region.Haiti
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:47]
+Name=Cat.native-country-region.South
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:48]
+Name=Cat.native-country-region.Laos
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Evaluator:1]
+EvaluatorType=DecisionTree
+NumInternalNodes=2
+SplitFeatures=I:1 I:2
+LTEChild=1 2
+GTChild=-1 -2
+Threshold=0.5 0.5
+Output=1 1 0
+
+[Evaluator:2]
+EvaluatorType=DecisionTree
+NumInternalNodes=5
+SplitFeatures=I:3 I:4 I:5 I:6 I:7
+LTEChild=1 2 3 4 5
+GTChild=-1 -2 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5 0.5
+Output=1 1 1 1 1 0
+
+[Evaluator:3]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=E:1 E:2 I:8 I:8
+SplitGain=4.90148266166068 2.33354921400773 1.13808322824716 0.946139966116641
+GainPValue=1.171e-18 1.222e-09 2.616e-05 1.178e-04
+LTEChild=3 2 -2 -1
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 5095.5 4792
+Output=-0.939622641509434 -0.557377049180328 0.353535353535354 1 0.666666666666667
+
+[Evaluator:4]
+EvaluatorType=DecisionTree
+NumInternalNodes=2
+SplitFeatures=I:1 I:2
+LTEChild=1 2
+GTChild=-1 -2
+Threshold=0.5 0.5
+Output=1 1 0
+
+[Evaluator:5]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:3 I:4 I:5 I:6
+LTEChild=1 2 3 4
+GTChild=-1 -2 -3 -4
+Threshold=0.5 0.5 0.5 0.5
+Output=1 1 1 1 0
+
+[Evaluator:6]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=E:4 E:5 I:8 I:8
+SplitGain=2.79327457556659 1.40544223678379 0.713319369811879 0.537299472359472
+GainPValue=1.264e-14 5.674e-07 3.297e-04 2.436e-04
+LTEChild=3 2 -2 -1
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 5095.5 4792
+Output=-0.748789346856104 -0.39712760037468 0.336144581428692 0.816754275453954 0.515587010648571
+
+[Evaluator:7]
+EvaluatorType=DecisionTree
+NumInternalNodes=2
+SplitFeatures=I:9 I:10
+LTEChild=1 2
+GTChild=-1 -2
+Threshold=0.5 0.5
+Output=1 1 0
+
+[Evaluator:8]
+EvaluatorType=DecisionTree
+NumInternalNodes=6
+SplitFeatures=I:3 I:4 I:5 I:6 I:11 I:7
+LTEChild=1 2 3 4 5 6
+GTChild=-1 -2 -3 -4 -5 -6
+Threshold=0.5 0.5 0.5 0.5 0.5 0.5
+Output=1 1 1 1 1 1 0
+
+[Evaluator:9]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=E:7 I:8 E:8 I:12
+SplitGain=1.64236173646617 0.908177201886967 0.611045759584491 0.520173608432503
+GainPValue=4.799e-11 2.741e-05 7.238e-04 2.739e-03
+LTEChild=-1 2 -2 -4
+GTChild=1 -3 3 -5
+Threshold=0.5 5095.5 0.5 208971.5
+Output=-0.62444676504895 -0.391477899528966 0.798245355006485 -0.137219458213507 0.461390764468794
+
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+[Evaluator:12]
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+[Evaluator:15]
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+[Evaluator:16]
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+[Evaluator:17]
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+[Evaluator:22]
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+[Evaluator:24]
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+[Evaluator:25]
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+[Evaluator:26]
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+[Evaluator:27]
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+[Evaluator:28]
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+[Evaluator:29]
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+[Evaluator:30]
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+[Evaluator:31]
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+[Evaluator:32]
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+[Evaluator:33]
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+[Evaluator:34]
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+[Evaluator:35]
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+[Evaluator:36]
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+[Evaluator:37]
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+[Evaluator:38]
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+[Evaluator:39]
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+[Evaluator:40]
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+[Evaluator:41]
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+[Evaluator:42]
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+[Evaluator:43]
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+[Evaluator:44]
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+[Evaluator:45]
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+[Evaluator:46]
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+[Evaluator:47]
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+[Evaluator:48]
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+[Evaluator:49]
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+[Evaluator:50]
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+[Evaluator:51]
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+
+[Evaluator:52]
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+[Evaluator:53]
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+SplitFeatures=E:51 E:52 I:12 I:12
+SplitGain=0.0379765968480972 0.0502937157071389 0.078858794113198 0.159339715865341
+GainPValue=1.291e-01 9.098e-02 1.105e-01 3.212e-02
+LTEChild=-1 -2 -3 -4
+GTChild=1 2 3 -5
+Threshold=0.5 0.5 115001 136814.5
+Output=-0.48957157464578 -0.200455693543699 -0.123662951790187 0.751300756127757 0.0853537145547922
+
+[Evaluator:54]
+EvaluatorType=Aggregator
+NumNodes=53
+Nodes=E:1 E:2 E:3 E:4 E:5 E:6 E:7 E:8 E:9 E:10 E:11 E:12 E:13 E:14 E:15 E:16 E:17 E:18 E:19 E:20
+Weights=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+Bias=0
+Type=Linear
+
+[Comments]
+C:0=Regression Tree Ensemble
+C:1=Generated using FastTree
+C:2=Created on %DateTime%
+C:3=PARAM:lr=0.25 ps=3 dt=+ cat=+ nl=5 mil=5 iter=20
+C:4=FG:I0:Cat.relationship.Own-child:8.20140469520056
+C:5=FG:I8:Num.capital-gain:6.80618757930535
+C:6=FG:I0:Cat.occupation.Machine-op-inspct:5.67334068537284
+C:7=FG:I0:Cat.marital-status.Never-married:3.29849586026056
+C:8=FG:I12:Num.fnlwgt:2.56786515484571
+C:9=FG:I20:Num.age:1.89728180508114
+C:10=FG:I0:Cat.education.11th:1.54788006843287
+C:11=FG:I17:Num.capital-loss:0.770619448146169
+C:12=FG:I26:Num.hours-per-week:0.68370919588418
+C:13=FG:I28:Cat.native-country-region.United-States:0.405013458752375
+C:14=FG:I22:Cat.education.Bachelors:0.242784582758851
+C:15=FG:I35:Num.education-num:0.210714496718656
+C:16=FG:I39:Cat.Workclass.Private:0.157512861310734
+C:17=FG:I9:Cat.marital-status.Married-civ-spouse:0.109286315671263
+C:18=FG:I43:Cat.ethnicity.Black:0.0574335511031935
+
+
+[Evaluator:55]
+EvaluatorType=Aggregator
+Type=Sigmoid
+Bias=0
+NumNodes=1
+Nodes=E:54
+Weights=0.5
+
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat.txt
new file mode 100644
index 0000000000..d594d9118b
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeCategoricalDisk-TrainTest-Census.Cat.txt
@@ -0,0 +1,501 @@
+Instance Label Score Probability Log-loss Assigned
+0 0 -8.024538 0.0177707821 0.025868356579734787 0
+1 0 -3.7248745 0.134419218 0.20825962621769711 0
+2 1 2.814909 0.803364158 0.31587399832298241 1
+3 1 5.94220448 0.9512514 0.07210144180169599 1
+4 0 -7.96151257 0.0183292776 0.026688906267117979 0
+5 0 -6.97646666 0.0296488889 0.043421229390926068 0
+6 0 -6.14291954 0.0442999825 0.065370249865000216 0
+7 1 2.34320974 0.763435 0.38942275424810857 1
+8 0 -7.602183 0.0218579229 0.031884060134051366 0
+9 0 -4.63767767 0.08957471 0.13538746043907665 0
+10 1 3.61779666 0.8592287 0.21888596115654355 1
+11 0 -1.42862713 0.3286464 0.57485524698979973 0
+12 0 -7.602183 0.0218579229 0.031884060134051366 0
+13 0 -2.697739 0.206055269 0.33288951393691241 0
+14 1 -0.58793366 0.427033 1.227580508873189 0
+15 1 2.5536952 0.7819127 0.35492058295285162 1
+16 0 -7.96151257 0.0183292776 0.026688906267117979 0
+17 0 -2.34658933 0.236259982 0.38884647568284175 0
+18 0 -7.428894 0.0237891953 0.034735375385246868 0
+19 1 3.641049 0.860629 0.21653660323618093 1
+20 1 1.12962914 0.6375658 0.64935387083351437 1
+21 0 -6.97646666 0.0296488889 0.043421229390926068 0
+22 0 -6.740466 0.03323882 0.048768552691487943 0
+23 0 -6.669291 0.03440158 0.050504780494324844 0
+24 0 -1.50516737 0.3202586 0.55694207499181514 0
+25 1 6.98051548 0.9704093 0.043334756675478453 1
+26 0 -7.96151257 0.0183292776 0.026688906267117979 0
+27 0 -4.65464449 0.088885285 0.13429538509732655 0
+28 0 -4.330678 0.102906525 0.1566697767460935 0
+29 0 -4.9453373 0.0777965561 0.11684304142085777 0
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-out.txt
new file mode 100644
index 0000000000..d5f2433c08
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-out.txt
@@ -0,0 +1,55 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=%Output% loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat}
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise on disk
+Processed 500 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise on disk
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 55 | 60 | 0.4783
+ negative || 17 | 368 | 0.9558
+ ||======================
+Precision || 0.7639 | 0.8598 |
+OVERALL 0/1 ACCURACY: 0.846000
+LOG LOSS/instance: 0.481805
+Test-set entropy (prior Log-Loss/instance): 0.778011
+LOG-LOSS REDUCTION (RIG): 0.380722
+AUC: 0.893281
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.893281 (0.0000)
+Accuracy: 0.846000 (0.0000)
+Positive precision: 0.763889 (0.0000)
+Positive recall: 0.478261 (0.0000)
+Negative precision: 0.859813 (0.0000)
+Negative recall: 0.955844 (0.0000)
+Log-loss: 0.481805 (0.0000)
+Log-loss reduction: 0.380722 (0.0000)
+F1 Score: 0.588235 (0.0000)
+AUPRC: 0.738040 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'Building term dictionary' started.
+[1] (%Time%) 500 examples Total Terms: 76
+[1] 'Building term dictionary' finished in %Time%.
+[2] 'FastTree disk-based bins initialization' started.
+[2] 'FastTree disk-based bins initialization' finished in %Time%.
+[3] 'FastTree disk-based bins initialization #2' started.
+[3] 'FastTree disk-based bins initialization #2' finished in %Time%.
+[4] 'FastTree training' started.
+[4] 'FastTree training' finished in %Time%.
+[5] 'Saving model' started.
+[5] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-rp.txt
new file mode 100644
index 0000000000..ae411db4ae
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-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
+0.893281 0.846 0.763889 0.478261 0.859813 0.955844 0.481805 0.380722 0.588235 0.73804 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+} dout=%Output% loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat} /lr:0.25;/dt:+;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt
new file mode 100644
index 0000000000..f0ad632661
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt
@@ -0,0 +1,30 @@
+
+Per-feature gain summary for the boosted tree ensemble:
+ marital-status.Married-civ-spouse 1
+ occupation.Prof-specialty 0.499978104303624
+ occupation.Exec-managerial 0.434935442345068
+ marital-status.Never-married 0.267286281351726
+ education.Doctorate 0.255205743594169
+ Workclass.Self-emp-inc 0.218913886097949
+ ethnicity.Asian-Pac-Islander 0.198007032992433
+ relationship.Husband 0.187623229061434
+ Workclass.Local-gov 0.186017717229329
+ native-country-region.Mexico 0.177614308223737
+ education.Bachelors 0.170936054067049
+ education.Masters 0.169804646788063
+ occupation.Farming-fishing 0.145811380173079
+ ethnicity.Black 0.137140760481446
+ occupation.Transport-moving 0.13022579853358
+ occupation.Other-service 0.12589916923037
+ education.Some-college 0.116092176092999
+ Workclass.Private 0.109401738173027
+ Workclass.? 0.109103172155946
+ education.Assoc-acdm 0.100323599151049
+ Workclass.Self-emp-not-inc 0.0973694292542764
+ relationship.Own-child 0.0957862810994456
+ education.11th 0.0905382862951382
+ occupation.Tech-support 0.0778673683865196
+ education.HS-grad 0.0727543074440302
+ education.7th-8th 0.0727320100839907
+ marital-status.Widowed 0.0634429881388435
+ Workclass.State-gov 0.0535351814497221
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini
new file mode 100644
index 0000000000..f306fe7d34
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini
@@ -0,0 +1,444 @@
+[TreeEnsemble]
+Inputs=28
+Evaluators=22
+
+[Input:1]
+Name=marital-status.Married-civ-spouse
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:2]
+Name=occupation.Prof-specialty
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:3]
+Name=occupation.Exec-managerial
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:4]
+Name=Workclass.Local-gov
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:5]
+Name=Workclass.Self-emp-inc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:6]
+Name=occupation.Transport-moving
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:7]
+Name=education.Doctorate
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:8]
+Name=native-country-region.Mexico
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:9]
+Name=ethnicity.Asian-Pac-Islander
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:10]
+Name=education.Masters
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:11]
+Name=marital-status.Never-married
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:12]
+Name=education.Bachelors
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:13]
+Name=relationship.Husband
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:14]
+Name=occupation.Other-service
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:15]
+Name=occupation.Farming-fishing
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:16]
+Name=Workclass.?
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:17]
+Name=education.Assoc-acdm
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:18]
+Name=relationship.Own-child
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:19]
+Name=education.11th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:20]
+Name=education.7th-8th
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:21]
+Name=marital-status.Widowed
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:22]
+Name=occupation.Tech-support
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:23]
+Name=ethnicity.Black
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:24]
+Name=Workclass.Private
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:25]
+Name=education.Some-college
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:26]
+Name=Workclass.State-gov
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:27]
+Name=education.HS-grad
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:28]
+Name=Workclass.Self-emp-not-inc
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Evaluator:1]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:3 I:4
+SplitGain=4.7088298291299 1.06377951357393 0.899536203027717 0.193365396473055
+GainPValue=5.502e-18 4.072e-05 1.661e-04 8.190e-02
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 0.5 0.5
+Output=-0.902621722846442 -0.398692810457516 0.529411764705882 0.3 0.333333333333333
+
+[Evaluator:2]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:3 I:5
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+[Evaluator:3]
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+[Evaluator:4]
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+[Evaluator:6]
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+[Evaluator:7]
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+[Evaluator:8]
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+[Evaluator:9]
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+[Evaluator:10]
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+[Evaluator:11]
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+[Evaluator:13]
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+[Evaluator:19]
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+[Evaluator:20]
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+[Evaluator:21]
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+Weights=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+Bias=0
+Type=Linear
+
+[Comments]
+C:0=Regression Tree Ensemble
+C:1=Generated using FastTree
+C:2=Created on %DateTime%
+C:3=PARAM:lr=0.25 ps=3 dt=+ nl=5 mil=5 iter=20
+C:4=FG:I1:marital-status.Married-civ-spouse:10.5224365222305
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+C:11=FG:I13:relationship.Husband:0.370415820013326
+C:12=FG:I4:Workclass.Local-gov:0.364103568598682
+C:13=FG:I8:native-country-region.Mexico:0.331949647533599
+C:14=FG:I12:education.Bachelors:0.307456488852699
+C:15=FG:I10:education.Masters:0.30339991585643
+C:16=FG:I15:occupation.Farming-fishing:0.223717087143772
+C:17=FG:I23:ethnicity.Black:0.197901652817432
+C:18=FG:I6:occupation.Transport-moving:0.178447460903355
+C:19=FG:I14:occupation.Other-service:0.166786940892928
+C:20=FG:I25:education.Some-college:0.141815016010588
+C:21=FG:I24:Workclass.Private:0.12594031021859
+C:22=FG:I16:Workclass.?:0.125253846023304
+C:23=FG:I17:education.Assoc-acdm:0.1059064773992
+C:24=FG:I28:Workclass.Self-emp-not-inc:0.0997611767187343
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+
+[Evaluator:22]
+EvaluatorType=Aggregator
+Type=Sigmoid
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+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt
new file mode 100644
index 0000000000..f0e217c24f
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt
@@ -0,0 +1,501 @@
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat-out.txt
new file mode 100644
index 0000000000..e66cab31ea
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat-out.txt
@@ -0,0 +1,55 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=%Output% loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat}
+Not adding a normalizer.
+Making per-feature arrays
+Changing data from row-wise to column-wise on disk
+Processed 500 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise on disk
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 75 | 40 | 0.6522
+ negative || 6 | 379 | 0.9844
+ ||======================
+Precision || 0.9259 | 0.9045 |
+OVERALL 0/1 ACCURACY: 0.908000
+LOG LOSS/instance: 0.353688
+Test-set entropy (prior Log-Loss/instance): 0.778011
+LOG-LOSS REDUCTION (RIG): 0.545395
+AUC: 0.958893
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.958893 (0.0000)
+Accuracy: 0.908000 (0.0000)
+Positive precision: 0.925926 (0.0000)
+Positive recall: 0.652174 (0.0000)
+Negative precision: 0.904535 (0.0000)
+Negative recall: 0.984416 (0.0000)
+Log-loss: 0.353688 (0.0000)
+Log-loss reduction: 0.545395 (0.0000)
+F1 Score: 0.765306 (0.0000)
+AUPRC: 0.895540 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime% Time elapsed(s): %Number%
+
+--- Progress log ---
+[1] 'Building term dictionary' started.
+[1] (%Time%) 500 examples Total Terms: 76
+[1] 'Building term dictionary' finished in %Time%.
+[2] 'FastTree disk-based bins initialization' started.
+[2] 'FastTree disk-based bins initialization' finished in %Time%.
+[3] 'FastTree disk-based bins initialization #2' started.
+[3] 'FastTree disk-based bins initialization #2' finished in %Time%.
+[4] 'FastTree training' started.
+[4] 'FastTree training' finished in %Time%.
+[5] 'Saving model' started.
+[5] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat-rp.txt
new file mode 100644
index 0000000000..ef966d89bf
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat-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
+0.958893 0.908 0.925926 0.652174 0.904535 0.984416 0.353688 0.545395 0.765306 0.89554 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+} dout=%Output% loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=%Data% out=%Output% seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} /lr:0.25;/dt:+;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat-summary.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat-summary.txt
new file mode 100644
index 0000000000..ddf1cabfbd
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat-summary.txt
@@ -0,0 +1,22 @@
+
+Per-feature gain summary for the boosted tree ensemble:
+ Cat.marital-status.Married-civ-spouse 1
+ Num.capital-gain 0.78247640388701
+ Num.fnlwgt 0.545416657387531
+ Num.education-num 0.531707897890713
+ Num.age 0.409622531262292
+ Cat.occupation.Prof-specialty 0.357582620367965
+ Num.capital-loss 0.327970655093426
+ Num.hours-per-week 0.307112374569836
+ Cat.occupation.Exec-managerial 0.168470795393402
+ Cat.ethnicity.White 0.12766472086777
+ Cat.education.Masters 0.124640495264293
+ Cat.relationship.Husband 0.112403865114159
+ Cat.marital-status.Never-married 0.110824550796253
+ Cat.occupation.Protective-serv 0.0989436478612388
+ Cat.education.Bachelors 0.0987203691811107
+ Cat.native-country-region.Mexico 0.0950276929435107
+ Cat.occupation.Other-service 0.0883199693237364
+ Cat.occupation.Farming-fishing 0.0758713488112266
+ Cat.ethnicity.Asian-Pac-Islander 0.0738301375676183
+ Cat.occupation.Transport-moving 0.0622812764538133
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat.ini b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat.ini
new file mode 100644
index 0000000000..344c35b89b
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat.ini
@@ -0,0 +1,388 @@
+[TreeEnsemble]
+Inputs=20
+Evaluators=22
+
+[Input:1]
+Name=Cat.marital-status.Married-civ-spouse
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:2]
+Name=Num.capital-gain
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:3]
+Name=Num.education-num
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:4]
+Name=Cat.occupation.Prof-specialty
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:5]
+Name=Num.capital-loss
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:6]
+Name=Num.fnlwgt
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:7]
+Name=Num.age
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:8]
+Name=Num.hours-per-week
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:9]
+Name=Cat.occupation.Exec-managerial
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:10]
+Name=Cat.relationship.Husband
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:11]
+Name=Cat.education.Masters
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:12]
+Name=Cat.marital-status.Never-married
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:13]
+Name=Cat.occupation.Protective-serv
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:14]
+Name=Cat.ethnicity.White
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:15]
+Name=Cat.native-country-region.Mexico
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:16]
+Name=Cat.occupation.Other-service
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:17]
+Name=Cat.ethnicity.Asian-Pac-Islander
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:18]
+Name=Cat.education.Bachelors
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:19]
+Name=Cat.occupation.Farming-fishing
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Input:20]
+Name=Cat.occupation.Transport-moving
+Transform=Linear
+Slope=1
+Intercept=0
+
+[Evaluator:1]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:3 I:2
+SplitGain=4.7088298291299 1.82393817313143 1.15134889109566 0.944729648284472
+GainPValue=5.502e-18 7.752e-08 2.025e-05 1.194e-04
+LTEChild=3 2 -2 -1
+GTChild=1 -3 -4 -5
+Threshold=0.5 5095.5 10.5 4792
+Output=-0.938697318007663 -0.421052631578947 1 0.233333333333333 0.666666666666667
+
+[Evaluator:2]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:4 I:2
+SplitGain=2.68797002190992 1.03782125239082 0.702469684950127 0.536490605065452
+GainPValue=5.720e-14 2.027e-05 5.830e-04 2.490e-04
+LTEChild=3 2 -2 -1
+GTChild=1 -3 -4 -5
+Threshold=0.5 5095.5 0.5 4792
+Output=-0.747963566988115 -0.271418660901274 0.803265329856317 0.402838788558462 0.515587010648571
+
+[Evaluator:3]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:2 I:5 I:6
+SplitGain=1.57896711216689 0.602950601535339 0.507933784729022 0.460067406857802
+GainPValue=1.986e-10 8.023e-04 2.839e-03 4.387e-03
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 5095.5 1881.5 207142.5
+Output=-0.616452312281396 -0.325227847195953 0.702953204648243 0.715005789478837 0.0879071517784774
+
+[Evaluator:4]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:3 I:6 I:7
+SplitGain=0.933961437525034 0.469845253691399 0.368778829041866 0.361434179229943
+GainPValue=1.498e-07 2.313e-03 6.995e-03 9.875e-03
+LTEChild=-1 2 -2 -4
+GTChild=1 -3 3 -5
+Threshold=0.5 10.5 115001 26.5
+Output=-0.540835682387761 -0.531147181613067 0.237930526342662 -0.85472434996025 0.00526136141356183
+
+[Evaluator:5]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:2 I:3 I:5 I:8
+SplitGain=0.728157533683411 0.553144315838373 0.406333943012827 0.320381360794014
+GainPValue=1.088e-06 2.448e-05 2.699e-04 1.127e-03
+LTEChild=1 2 3 -1
+GTChild=-2 -3 -4 -5
+Threshold=4792 15.5 1881.5 44.5
+Output=-0.416955329633573 0.584589466495732 1.07159032482301 0.545790389845197 -0.0736235783382999
+
+[Evaluator:6]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:7 I:6 I:8
+SplitGain=0.515353351089896 0.263607430449458 0.321439031330917 0.304206136711201
+GainPValue=1.898e-05 1.646e-02 8.649e-03 7.992e-03
+LTEChild=-1 -2 3 -3
+GTChild=1 2 -4 -5
+Threshold=0.5 26.5 207142.5 37.5
+Output=-0.453041322781326 -0.639214220190514 -0.551788762308723 0.274328102623981 0.0108302779342166
+
+[Evaluator:7]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:2 I:4 I:7 I:9
+SplitGain=0.429211498172382 0.37130253655033 0.245607013698752 0.240170720708446
+GainPValue=5.826e-05 2.090e-04 1.458e-02 2.066e-03
+LTEChild=1 3 -3 -1
+GTChild=-2 2 -4 -5
+Threshold=6897.5 0.5 38.5 0.5
+Output=-0.325018558560221 0.709504798114759 -0.0978229810975667 0.619123743179419 0.0738169429913811
+
+[Evaluator:8]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:7 I:8 I:6 I:3
+SplitGain=0.324507600154794 0.231880035343412 0.218742301145431 0.158753505887515
+GainPValue=3.333e-04 7.590e-03 1.751e-02 2.080e-02
+LTEChild=-1 3 -3 -2
+GTChild=1 2 -4 -5
+Threshold=26.5 40.5 343826.5 14.5
+Output=-0.538717070438754 -0.163437602533054 0.107912639659195 0.928282843335679 0.642680985019362
+
+[Evaluator:9]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:1 I:6 I:6 I:6
+SplitGain=0.263067945107473 0.221497887988851 0.33860104205235 0.320394503285078
+GainPValue=9.839e-04 2.092e-02 5.916e-03 6.456e-03
+LTEChild=-1 -2 -3 -4
+GTChild=1 2 3 -5
+Threshold=0.5 117215.5 135637 153300
+Output=-0.361179752457602 -0.197836735178024 1.09707453767085 -0.612339516540567 0.157332113494155
+
+[Evaluator:10]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:2 I:3 I:10 I:7
+SplitGain=0.237645949149002 0.171230238633491 0.136822585351253 0.187108623295558
+GainPValue=1.362e-03 7.096e-03 1.588e-02 3.971e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=4792 15.5 0.5 60
+Output=-0.339659136475996 0.537069257542093 0.862924154545784 0.0657893860383729 -0.427847068751094
+
+[Evaluator:11]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:4 I:6 I:6 I:11
+SplitGain=0.158028250346348 0.178485982354828 0.206838765522749 0.168233947018622
+GainPValue=8.034e-03 2.111e-02 1.419e-02 4.109e-02
+LTEChild=-1 3 -3 -2
+GTChild=1 2 -4 -5
+Threshold=0.5 100319 177610 0.5
+Output=-0.119308561404354 0.228243402552109 1.05719705454013 0.0598276043939003 -0.957602680739738
+
+[Evaluator:12]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:2 I:12 I:3 I:6
+SplitGain=0.146923659119402 0.13300478378088 0.141575583388046 0.115166085208399
+GainPValue=9.451e-03 1.462e-02 3.858e-02 7.112e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=7493 0.5 8.5 111675.5
+Output=-0.338640974417467 0.676917239927446 -0.464078313783936 -0.155141823898237 0.134390638105366
+
+[Evaluator:13]
+EvaluatorType=DecisionTree
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+SplitFeatures=I:7 I:8 I:13 I:14
+SplitGain=0.113882941049341 0.0947541242081512 0.106015932587327 0.103800129357575
+GainPValue=2.065e-02 6.702e-02 4.259e-02 7.898e-02
+LTEChild=-1 2 -2 -3
+GTChild=1 3 -4 -5
+Threshold=26.5 40.5 0.5 0.5
+Output=-0.476743492588366 -0.0788770608124479 -0.411073358570253 0.744504044683463 0.213969436609267
+
+[Evaluator:14]
+EvaluatorType=DecisionTree
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+SplitGain=0.0885736781849435 0.0906152230827666 0.0920787482727172 0.096674480488273
+GainPValue=3.903e-02 3.534e-02 3.652e-02 7.200e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=1881.5 7493 0.5 100319
+Output=-0.120315893237392 0.392880244098873 0.644392747165095 -0.315040034480276 0.360910725076097
+
+[Evaluator:15]
+EvaluatorType=DecisionTree
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+GainPValue=5.334e-02 1.012e-01 1.097e-01 2.116e-02
+LTEChild=-1 2 3 -2
+GTChild=1 -3 -4 -5
+Threshold=0.5 0.5 207142.5 189250
+Output=-0.237552799101888 0.0627032836526124 -0.672146337661497 0.209890429165221 -0.506493119136646
+
+[Evaluator:16]
+EvaluatorType=DecisionTree
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+SplitFeatures=I:9 I:5 I:14 I:7
+SplitGain=0.0671873028053795 0.161995914993803 0.0726967789328359 0.0880669874047782
+GainPValue=6.741e-02 9.100e-03 5.356e-02 3.922e-02
+LTEChild=2 -2 -1 -4
+GTChild=1 -3 3 -5
+Threshold=0.5 1457 0.5 60
+Output=-0.426029039803156 0.0843279580735084 0.877114094236869 0.0206591289893379 -0.408935848550922
+
+[Evaluator:17]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:7 I:8 I:16 I:7
+SplitGain=0.0657325821949111 0.0701644214009786 0.0844720669896727 0.167086317488132
+GainPValue=6.738e-02 1.024e-01 8.596e-02 1.815e-02
+LTEChild=-1 -2 3 -3
+GTChild=1 2 -4 -5
+Threshold=26.5 31 0.5 27.5
+Output=-0.44018259816661 -0.323439227342382 1.35075902903175 -0.448762931500328 0.0782616670819337
+
+[Evaluator:18]
+EvaluatorType=DecisionTree
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+SplitFeatures=I:3 I:17 I:4 I:18
+SplitGain=0.0583015498348141 0.059028623710292 0.0607965965868098 0.105537996112979
+GainPValue=8.120e-02 7.980e-02 7.563e-02 4.257e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=15.5 0.5 0.5 0.5
+Output=-0.0475499334266732 0.758934721534646 -0.579100267851108 -0.0207753186547962 0.613424150388954
+
+[Evaluator:19]
+EvaluatorType=DecisionTree
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+SplitFeatures=I:2 I:19 I:1 I:6
+SplitGain=0.0518864329133906 0.0623377197096143 0.0646445617187989 0.102563250702328
+GainPValue=9.681e-02 7.325e-02 6.841e-02 9.478e-02
+LTEChild=1 2 -1 -4
+GTChild=-2 -3 3 -5
+Threshold=6897.5 0.5 0.5 153300
+Output=-0.239443828579011 0.635670391470562 -0.491899269148587 -0.0902190626578666 0.15193315669661
+
+[Evaluator:20]
+EvaluatorType=DecisionTree
+NumInternalNodes=4
+SplitFeatures=I:20 I:3 I:3 I:3
+SplitGain=0.0420059017164206 0.174259979330403 0.0933876246666109 0.0897042435901111
+GainPValue=1.325e-01 3.487e-02 8.920e-02 2.398e-02
+LTEChild=3 -2 -3 -1
+GTChild=1 2 -4 -5
+Threshold=0.5 8.5 9.5 8.5
+Output=-0.468182078145119 1.40393617893461 0.309735584528877 -0.719340829777538 0.0193254769476334
+
+[Evaluator:21]
+EvaluatorType=Aggregator
+NumNodes=20
+Nodes=E:1 E:2 E:3 E:4 E:5 E:6 E:7 E:8 E:9 E:10 E:11 E:12 E:13 E:14 E:15 E:16 E:17 E:18 E:19 E:20
+Weights=1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
+Bias=0
+Type=Linear
+
+[Comments]
+C:0=Regression Tree Ensemble
+C:1=Generated using FastTree
+C:2=Created on %DateTime%
+C:3=PARAM:lr=0.25 ps=3 dt=+ nl=5 mil=5 iter=20
+C:4=FG:I1:Cat.marital-status.Married-civ-spouse:10.8291732597992
+C:5=FG:I2:Num.capital-gain:6.63037057652787
+C:6=FG:I6:Num.fnlwgt:3.22145520746603
+C:7=FG:I3:Num.education-num:3.06155118595643
+C:8=FG:I7:Num.age:1.81703367496567
+C:9=FG:I4:Cat.occupation.Prof-specialty:1.38467581670633
+C:10=FG:I5:Num.capital-loss:1.1648373209206
+C:11=FG:I8:Num.hours-per-week:1.02138607845776
+C:12=FG:I9:Cat.occupation.Exec-managerial:0.307358023513825
+C:13=FG:I14:Cat.ethnicity.White:0.176496908290411
+C:14=FG:I11:Cat.education.Masters:0.168233947018622
+C:15=FG:I10:Cat.relationship.Husband:0.136822585351253
+C:16=FG:I12:Cat.marital-status.Never-married:0.13300478378088
+C:17=FG:I13:Cat.occupation.Protective-serv:0.106015932587327
+C:18=FG:I18:Cat.education.Bachelors:0.105537996112979
+C:19=FG:I15:Cat.native-country-region.Mexico:0.0977902763944078
+C:20=FG:I16:Cat.occupation.Other-service:0.0844720669896727
+C:21=FG:I19:Cat.occupation.Farming-fishing:0.0623377197096143
+C:22=FG:I17:Cat.ethnicity.Asian-Pac-Islander:0.059028623710292
+C:23=FG:I20:Cat.occupation.Transport-moving:0.0420059017164206
+
+
+[Evaluator:22]
+EvaluatorType=Aggregator
+Type=Sigmoid
+Bias=0
+NumNodes=1
+Nodes=E:21
+Weights=0.5
+
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat.txt
new file mode 100644
index 0000000000..cc6f11880d
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-Census.Cat.txt
@@ -0,0 +1,501 @@
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..4f322497a7
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,54 @@
+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
+Changing data from row-wise to column-wise on disk
+Warning: 16 of 699 examples will be skipped due to missing feature values
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 240 | 1 | 0.9959
+ negative || 13 | 445 | 0.9716
+ ||======================
+Precision || 0.9486 | 0.9978 |
+OVERALL 0/1 ACCURACY: 0.979971
+LOG LOSS/instance: 0.092572
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 0.900387
+AUC: 0.995370
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.995370 (0.0000)
+Accuracy: 0.979971 (0.0000)
+Positive precision: 0.948617 (0.0000)
+Positive recall: 0.995851 (0.0000)
+Negative precision: 0.997758 (0.0000)
+Negative recall: 0.971616 (0.0000)
+Log-loss: 0.092572 (0.0000)
+Log-loss reduction: 0.900387 (0.0000)
+F1 Score: 0.971660 (0.0000)
+AUPRC: 0.970606 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %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 disk-based bins initialization #2' started.
+[2] 'FastTree disk-based bins initialization #2' finished in %Time%.
+[3] 'FastTree training' started.
+[3] 'FastTree training' finished in %Time%.
+[4] 'Saving model' started.
+[4] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..eaf2efe1d3
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/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 0.900387 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/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..3e8aca5beb
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDisk-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDrop-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDrop-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..3f94c48ebf
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDrop-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,60 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 tdrop=0.5 lr=0.25 iter=20 mb=255} 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
+Warning: Skipped 16 instances with missing features during training
+Processed 683 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise
+Warning: Skipped 16 instances with missing features during training
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 234 | 7 | 0.9710
+ negative || 28 | 430 | 0.9389
+ ||======================
+Precision || 0.8931 | 0.9840 |
+OVERALL 0/1 ACCURACY: 0.949928
+LOG LOSS/instance: 0.626065
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): 0.326318
+AUC: 0.987371
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.987371 (0.0000)
+Accuracy: 0.949928 (0.0000)
+Positive precision: 0.893130 (0.0000)
+Positive recall: 0.970954 (0.0000)
+Negative precision: 0.983982 (0.0000)
+Negative recall: 0.938865 (0.0000)
+Log-loss: 0.626065 (0.0000)
+Log-loss reduction: 0.326318 (0.0000)
+F1 Score: 0.930417 (0.0000)
+AUPRC: 0.942315 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %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] 'FastTree data preparation #2' started.
+[4] 'FastTree data preparation #2' finished in %Time%.
+[5] 'FastTree feature conversion #2' started.
+[5] 'FastTree feature conversion #2' finished in %Time%.
+[6] 'FastTree training' started.
+[6] 'FastTree training' finished in %Time%.
+[7] 'Saving model' started.
+[7] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDrop-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDrop-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..3731ebe8df
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDrop-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 /tdrop /nl /mil /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.987371 0.949928 0.89313 0.970954 0.983982 0.938865 0.626065 0.326318 0.930417 0.942315 0.25 0.5 5 5 20 FastTreeBinaryClassification %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{nl=5 mil=5 tdrop=0.5 lr=0.25 iter=20 mb=255} 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;/tdrop:0.5;/nl:5;/mil:5;/iter:20
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDrop-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDrop-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..3836e93f38
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeDrop-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
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diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeHighMinDocs-TrainTest-breast-cancer-out.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeHighMinDocs-TrainTest-breast-cancer-out.txt
new file mode 100644
index 0000000000..cc0142cc56
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeHighMinDocs-TrainTest-breast-cancer-out.txt
@@ -0,0 +1,61 @@
+maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{mil=10000 iter=5} 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
+Warning: Skipped 16 instances with missing features during training
+Processed 683 instances
+Binning and forming Feature objects
+Changing data from row-wise to column-wise
+Warning: Skipped 16 instances with missing features during training
+Reserved memory for tree learner: %Number% bytes
+Starting to train ...
+Warning: 5 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset.
+Not training a calibrator because it is not needed.
+TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0))
+Confusion table
+ ||======================
+PREDICTED || positive | negative | Recall
+TRUTH ||======================
+ positive || 0 | 241 | 0.0000
+ negative || 0 | 458 | 1.0000
+ ||======================
+Precision || 0.0000 | 0.6552 |
+OVERALL 0/1 ACCURACY: 0.655222
+LOG LOSS/instance: 1.000000
+Test-set entropy (prior Log-Loss/instance): 0.929318
+LOG-LOSS REDUCTION (RIG): -0.076058
+AUC: 0.500000
+
+OVERALL RESULTS
+---------------------------------------
+AUC: 0.500000 (0.0000)
+Accuracy: 0.655222 (0.0000)
+Positive precision: 0.000000 (0.0000)
+Positive recall: 0.000000 (0.0000)
+Negative precision: 0.655222 (0.0000)
+Negative recall: 1.000000 (0.0000)
+Log-loss: 1.000000 (0.0000)
+Log-loss reduction: -0.076058 (0.0000)
+F1 Score: 0.000000 (0.0000)
+AUPRC: 0.415719 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %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] 'FastTree data preparation #2' started.
+[4] 'FastTree data preparation #2' finished in %Time%.
+[5] 'FastTree feature conversion #2' started.
+[5] 'FastTree feature conversion #2' finished in %Time%.
+[6] 'FastTree training' started.
+[6] 'FastTree training' finished in %Time%.
+[7] 'Saving model' started.
+[7] 'Saving model' finished in %Time%.
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeHighMinDocs-TrainTest-breast-cancer-rp.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeHighMinDocs-TrainTest-breast-cancer-rp.txt
new file mode 100644
index 0000000000..d1d605b7e5
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeHighMinDocs-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 /mil /iter Learner Name Train Dataset Test Dataset Results File Run Time Physical Memory Virtual Memory Command Line Settings
+0.5 0.655222 0 0 0.655222 1 1 -0.076058 0 0.415719 10000 5 FastTreeBinaryClassification %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=FastTreeBinaryClassification{mil=10000 iter=5} 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 /mil:10000;/iter:5
+
diff --git a/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeHighMinDocs-TrainTest-breast-cancer.txt b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeHighMinDocs-TrainTest-breast-cancer.txt
new file mode 100644
index 0000000000..a30a2f04cc
--- /dev/null
+++ b/test/BaselineOutput/Common/FastTreeBinaryClassification/win-x86/FastTreeHighMinDocs-TrainTest-breast-cancer.txt
@@ -0,0 +1,700 @@
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diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
index f038605fd4..f7abc2fc1f 100644
--- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
+++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
@@ -187,7 +187,7 @@ public void MulticlassSdcaTest()
///
/// Multiclass Logistic Regression test with a tree featurizer.
///
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Multiclass")]
[TestCategory("Logistic Regression")]
[TestCategory("FastTree")]
@@ -246,7 +246,7 @@ public void KMeansClusteringTest()
Done();
}
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
[TestCategory("SDCA")]
public void LinearClassifierTest()
@@ -267,7 +267,7 @@ public void LinearClassifierTest()
///
///A test for binary classifiers
///
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
public void BinaryClassifierLogisticRegressionTest()
{
@@ -294,7 +294,7 @@ public void BinaryClassifierSymSgdTest()
Done();
}
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
public void BinaryClassifierTesterThresholdingTest()
{
@@ -320,7 +320,7 @@ public void BinaryClassifierLogisticRegressionNormTest()
///
///A test for binary classifiers with non-negative coefficients
///
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
public void BinaryClassifierLogisticRegressionNonNegativeTest()
{
@@ -352,7 +352,7 @@ public void BinaryClassifierLogisticRegressionBinNormTest()
///
///A test for binary classifiers
///
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
public void BinaryClassifierLogisticRegressionGaussianNormTest()
{
@@ -389,7 +389,7 @@ public void BinaryClassifierFastRankClassificationTest()
///
///A test for binary classifiers
///
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
[TestCategory("FastForest")]
public void FastForestClassificationTest()
@@ -456,7 +456,7 @@ public void WeightingFastForestRegressionPredictorsTest()
Done();
}
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
[TestCategory("FastTree")]
public void FastTreeBinaryClassificationTest()
@@ -972,7 +972,7 @@ private void CombineAndTestEnsembles(IDataView idv, string name, string options,
}
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
[TestCategory("FastTree")]
public void FastTreeBinaryClassificationCategoricalSplitTest()
@@ -1011,7 +1011,7 @@ public void FastTreeRegressionCategoricalSplitTest()
Done();
}
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
[TestCategory("FastTree")]
public void FastTreeBinaryClassificationNoOpGroupIdTest()
@@ -1031,7 +1031,7 @@ public void FastTreeBinaryClassificationNoOpGroupIdTest()
Done();
}
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Binary")]
[TestCategory("FastTree")]
public void FastTreeHighMinDocsTest()
@@ -1630,7 +1630,7 @@ public void PAVCalibratorPerceptronTest()
///
///A test for random calibrators
///
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
[TestCategory("Calibrator")]
public void RandomCalibratorPerceptronTest()
{
diff --git a/test/Microsoft.ML.TestFramework/TestCommandBase.cs b/test/Microsoft.ML.TestFramework/TestCommandBase.cs
index 42edf105b3..425fbab697 100644
--- a/test/Microsoft.ML.TestFramework/TestCommandBase.cs
+++ b/test/Microsoft.ML.TestFramework/TestCommandBase.cs
@@ -868,7 +868,7 @@ public void CommandCrossValidation()
Done();
}
- [X64Fact("x86 output differs from Baseline")]
+ [Fact]
public void CommandCrossValidationKeyLabelWithFloatKeyValues()
{
RunMTAThread(() =>