diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt
new file mode 100644
index 0000000000..a86a1611ec
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt
@@ -0,0 +1,58 @@
+maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
+Training calibrator.
+PAV calibrator:  piecewise function approximation has 6 components.
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
+Training calibrator.
+PAV calibrator:  piecewise function approximation has 6 components.
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      128 |        6 | 0.9552
+ negative ||        7 |      213 | 0.9682
+          ||======================
+Precision ||   0.9481 |   0.9726 |
+OVERALL 0/1 ACCURACY: 0.963277
+LOG LOSS/instance:  Infinity
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): -Infinity
+AUC:                0.994233
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||       97 |        8 | 0.9238
+ negative ||        2 |      222 | 0.9911
+          ||======================
+Precision ||   0.9798 |   0.9652 |
+OVERALL 0/1 ACCURACY: 0.969605
+LOG LOSS/instance:  0.220291
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 75.616820
+AUC:                0.997491
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995862 (0.0016)
+Accuracy:           0.966441 (0.0032)
+Positive precision: 0.963973 (0.0158)
+Positive recall:    0.939517 (0.0157)
+Negative precision: 0.968910 (0.0037)
+Negative recall:    0.979627 (0.0114)
+Log-loss:           Infinity (NaN)
+Log-loss reduction: -Infinity (NaN)
+F1 Score:           0.951327 (0.0003)
+AUPRC:              0.991949 (0.0025)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt
new file mode 100644
index 0000000000..45154c87f5
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt
@@ -0,0 +1,4 @@
+LinearSVM
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	Infinity	-Infinity	0.951327	0.991949	0.03	100	LinearSVM	%Data%		%Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	
+
diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt
new file mode 100644
index 0000000000..b337fd78da
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Probability	Log-loss	Assigned
+5	1	2.5100162	1	0	1
+6	0	-0.42988503	0.5	1	0
+8	0	-1.39884758	1E-15	1.4415419267167138E-15	0
+9	0	-1.20900154	1E-15	1.4415419267167138E-15	0
+10	0	-1.48176765	1E-15	1.4415419267167138E-15	0
+11	0	-1.41764426	1E-15	1.4415419267167138E-15	0
+18	1	1.4347012	1	0	1
+20	1	1.1132617	1	0	1
+21	1	1.42924047	1	0	1
+25	1	0.161359549	0.8666667	0.20645086423799175	1
+28	0	-1.41764426	1E-15	1.4415419267167138E-15	0
+31	0	-1.36339951	1E-15	1.4415419267167138E-15	0
+32	1	1.25826859	1	0	1
+35	0	-1.41764426	1E-15	1.4415419267167138E-15	0
+37	0	-0.835641861	1E-15	1.4415419267167138E-15	0
+40	0				
+41	1	0.227130175	0.8666667	0.20645086423799175	1
+44	1	1.61423349	1	0	1
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+46	1	1.08742118	1	0	1
+48	0	-1.118499	1E-15	1.4415419267167138E-15	0
+50	1	0.137539148	0.8666667	0.20645086423799175	1
+51	1	-0.2256087	0.714285731	0.48542679277371198	0
+52	1	0.825196	1	0	1
+54	1	1.26949811	1	0	1
+56	1	1.32781029	1	0	1
+60	1	0.291453838	0.8666667	0.20645086423799175	1
+63	1	-0.03475213	0.8666667	0.20645086423799175	0
+64	0	-1.49114561	1E-15	1.4415419267167138E-15	0
+66	0	-1.216397	1E-15	1.4415419267167138E-15	0
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+71	1	1.14647889	1	0	1
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+73	1	1.69674277	1	0	1
+74	1	0.2194643	0.8666667	0.20645086423799175	1
+76	0	-1.27802086	1E-15	1.4415419267167138E-15	0
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+145	0				
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+150	0	-1.48176765	1E-15	1.4415419267167138E-15	0
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+161	0	-1.1599288	1E-15	1.4415419267167138E-15	0
+164	0				
+167	1	1.07981849	1	0	1
+169	0	-1.5413084	1E-15	1.4415419267167138E-15	0
+171	0	-1.43690062	1E-15	1.4415419267167138E-15	0
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+181	0	-1.54539037	1E-15	1.4415419267167138E-15	0
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+191	1	2.29551339	1	0	1
+192	0	-1.19714069	1E-15	1.4415419267167138E-15	0
+196	0	1.06304407	1	Infinity	1
+198	0	-1.54539037	1E-15	1.4415419267167138E-15	0
+199	0	-1.34414315	1E-15	1.4415419267167138E-15	0
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+204	0	-1.43690062	1E-15	1.4415419267167138E-15	0
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+209	0	-1.18776274	1E-15	1.4415419267167138E-15	0
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+216	0	-1.49114561	1E-15	1.4415419267167138E-15	0
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+248	0	-0.934056163	1E-15	1.4415419267167138E-15	0
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+250	0	-1.41270852	1E-15	1.4415419267167138E-15	0
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+275	0				
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+291	0	-1.43690062	1E-15	1.4415419267167138E-15	0
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+297	0				
+299	1	1.13769388	1	0	1
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+317	1	1.81665158	1	0	1
+319	0	0.285441637	0.8666667	2.9068906815998465	1
+321	0				
+323	1	0.8990345	1	0	1
+327	0	-1.49114561	1E-15	1.4415419267167138E-15	0
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+331	0	-1.12463534	1E-15	1.4415419267167138E-15	0
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+333	1	0.9059088	1	0	1
+336	1	1.07013011	1	0	1
+338	0	-1.53601253	1E-15	1.4415419267167138E-15	0
+343	0	-1.54539037	1E-15	1.4415419267167138E-15	0
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+346	0	-0.9119513	1E-15	1.4415419267167138E-15	0
+347	0	-1.47732091	1E-15	1.4415419267167138E-15	0
+348	1	-0.20377481	0.714285731	0.48542679277371198	0
+349	1	0.510544062	0.8666667	0.20645086423799175	1
+350	0	-1.15348744	1E-15	1.4415419267167138E-15	0
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+371	0	-1.4033072	1E-15	1.4415419267167138E-15	0
+373	0	-1.14819169	1E-15	1.4415419267167138E-15	0
+376	0	-1.49114561	1E-15	1.4415419267167138E-15	0
+377	0	-1.53601253	1E-15	1.4415419267167138E-15	0
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+384	0	-1.47188914	1E-15	1.4415419267167138E-15	0
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+395	0	-1.34414315	1E-15	1.4415419267167138E-15	0
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+434	0	1.07432151	1	Infinity	1
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diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-out.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-out.txt
new file mode 100644
index 0000000000..ab78c00f64
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-out.txt
@@ -0,0 +1,58 @@
+maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali={} dout=%Output% data=%Data% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
+Not training a calibrator because a valid calibrator trainer was not provided.
+Warning: Data does not contain a probability column. Will not output the Log-loss column
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
+Not training a calibrator because a valid calibrator trainer was not provided.
+Warning: Data does not contain a probability column. Will not output the Log-loss column
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      128 |        6 | 0.9552
+ negative ||        7 |      213 | 0.9682
+          ||======================
+Precision ||   0.9481 |   0.9726 |
+OVERALL 0/1 ACCURACY: 0.963277
+LOG LOSS/instance:      NaN
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): 0.000000
+AUC:                0.994233
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||       97 |        8 | 0.9238
+ negative ||        2 |      222 | 0.9911
+          ||======================
+Precision ||   0.9798 |   0.9652 |
+OVERALL 0/1 ACCURACY: 0.969605
+LOG LOSS/instance:      NaN
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.000000
+AUC:                0.997491
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995862 (0.0016)
+Accuracy:           0.966441 (0.0032)
+Positive precision: 0.963973 (0.0158)
+Positive recall:    0.939517 (0.0157)
+Negative precision: 0.968910 (0.0037)
+Negative recall:    0.979627 (0.0114)
+Log-loss:               NaN (NaN)
+Log-loss reduction: 0.000000 (0.0000)
+F1 Score:           0.951327 (0.0003)
+AUPRC:              0.991949 (0.0025)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-rp.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-rp.txt
new file mode 100644
index 0000000000..b12b8ce3c0
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-rp.txt
@@ -0,0 +1,4 @@
+LinearSVM
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	NaN	0	0.951327	0.991949	0.03	100	LinearSVM	%Data%		%Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali={} dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	
+
diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt
new file mode 100644
index 0000000000..fed143de3d
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Assigned
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diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt
new file mode 100644
index 0000000000..8648210735
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt
@@ -0,0 +1,39 @@
+maml.exe TrainTest test=%Data% tr=LinearSVM{iter=100 lambda=0.03} cali=PAV dout=%Output% data=%Data% out=%Output% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter)
+Training calibrator.
+PAV calibrator:  piecewise function approximation has 8 components.
+Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3499 (239.0/(239.0+444.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      226 |       13 | 0.9456
+ negative ||        9 |      435 | 0.9797
+          ||======================
+Precision ||   0.9617 |   0.9710 |
+OVERALL 0/1 ACCURACY: 0.967789
+LOG LOSS/instance:  0.084588
+Test-set entropy (prior Log-Loss/instance): 0.934003
+LOG-LOSS REDUCTION (RIG): 90.943543
+AUC:                0.995797
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995797 (0.0000)
+Accuracy:           0.967789 (0.0000)
+Positive precision: 0.961702 (0.0000)
+Positive recall:    0.945607 (0.0000)
+Negative precision: 0.970982 (0.0000)
+Negative recall:    0.979730 (0.0000)
+Log-loss:           0.084588 (0.0000)
+Log-loss reduction: 90.943543 (0.0000)
+F1 Score:           0.953586 (0.0000)
+AUPRC:              0.991453 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt
new file mode 100644
index 0000000000..2f6bd4df6f
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt
@@ -0,0 +1,4 @@
+LinearSVM
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.995797	0.967789	0.961702	0.945607	0.970982	0.97973	0.084588	90.94354	0.953586	0.991453	0.03	100	LinearSVM	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LinearSVM{iter=100 lambda=0.03} cali=PAV dout=%Output% data=%Data% out=%Output% seed=1	/lambda:0.03;/iter:100	
+
diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt
new file mode 100644
index 0000000000..9c673bc10a
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Probability	Log-loss	Assigned
+0	0	-1.092935	1E-15	1.4415419267167138E-15	0
+1	0	0.7545147	0.826086938	2.5235618055722013	1
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+5	1	2.51247835	1	0	1
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+14	1	1.69779134	1	0	1
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diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt
new file mode 100644
index 0000000000..20ae32f902
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt
@@ -0,0 +1,39 @@
+maml.exe TrainTest test=%Data% tr=LinearSVM{iter=100 lambda=0.03} cali={} dout=%Output% data=%Data% out=%Output% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter)
+Not training a calibrator because a valid calibrator trainer was not provided.
+Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3499 (239.0/(239.0+444.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      226 |       13 | 0.9456
+ negative ||        9 |      435 | 0.9797
+          ||======================
+Precision ||   0.9617 |   0.9710 |
+OVERALL 0/1 ACCURACY: 0.967789
+LOG LOSS/instance:      NaN
+Test-set entropy (prior Log-Loss/instance): 0.934003
+LOG-LOSS REDUCTION (RIG): 0.000000
+AUC:                0.995797
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995797 (0.0000)
+Accuracy:           0.967789 (0.0000)
+Positive precision: 0.961702 (0.0000)
+Positive recall:    0.945607 (0.0000)
+Negative precision: 0.970982 (0.0000)
+Negative recall:    0.979730 (0.0000)
+Log-loss:               NaN (0.0000)
+Log-loss reduction: 0.000000 (0.0000)
+F1 Score:           0.953586 (0.0000)
+AUPRC:              0.991453 (0.0000)
+
+---------------------------------------
+Warning: Data does not contain a probability column. Will not output the Log-loss column
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt
new file mode 100644
index 0000000000..4395e7b3b5
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt
@@ -0,0 +1,4 @@
+LinearSVM
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.995797	0.967789	0.961702	0.945607	0.970982	0.97973	NaN	0	0.953586	0.991453	0.03	100	LinearSVM	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LinearSVM{iter=100 lambda=0.03} cali={} dout=%Output% data=%Data% out=%Output% seed=1	/lambda:0.03;/iter:100	
+
diff --git a/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt
new file mode 100644
index 0000000000..c5e41e5240
--- /dev/null
+++ b/ZBaselines/SingleDebug/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Assigned
+0	0	-1.092935	0
+1	0	0.7545147	1
+2	0	-1.13008308	0
+3	0	0.631343842	1
+4	0	-1.07333851	0
+5	1	2.51247835	1
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+20	1	1.133441	1
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+662	0	-1.30470824	0
+663	0	-1.30470824	0
+664	0	-1.19905353	0
+665	0	-1.49238932	0
+666	0	-0.9982786	0
+667	0	-1.24095953	0
+668	1	0.43660593	1
+669	1	1.72629261	1
+670	1	1.353138	1
+671	0	-1.13039923	0
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+684	0	-1.49238932	0
+685	0	-1.49238932	0
+686	0	-1.49238932	0
+687	0	-1.17473149	0
+688	0	-1.27035248	0
+689	0	-1.35580492	0
+690	0	-1.39878058	0
+691	1	1.03535128	1
+692	0	-1.34436476	0
+693	0	-1.27315009	0
+694	0	-1.1918596	0
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+696	1	1.42499018	1
+697	1	0.7907071	1
+698	1	1.00806427	1
diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt
new file mode 100644
index 0000000000..a86a1611ec
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-out.txt
@@ -0,0 +1,58 @@
+maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
+Training calibrator.
+PAV calibrator:  piecewise function approximation has 6 components.
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
+Training calibrator.
+PAV calibrator:  piecewise function approximation has 6 components.
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      128 |        6 | 0.9552
+ negative ||        7 |      213 | 0.9682
+          ||======================
+Precision ||   0.9481 |   0.9726 |
+OVERALL 0/1 ACCURACY: 0.963277
+LOG LOSS/instance:  Infinity
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): -Infinity
+AUC:                0.994233
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||       97 |        8 | 0.9238
+ negative ||        2 |      222 | 0.9911
+          ||======================
+Precision ||   0.9798 |   0.9652 |
+OVERALL 0/1 ACCURACY: 0.969605
+LOG LOSS/instance:  0.220291
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 75.616820
+AUC:                0.997491
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995862 (0.0016)
+Accuracy:           0.966441 (0.0032)
+Positive precision: 0.963973 (0.0158)
+Positive recall:    0.939517 (0.0157)
+Negative precision: 0.968910 (0.0037)
+Negative recall:    0.979627 (0.0114)
+Log-loss:           Infinity (NaN)
+Log-loss reduction: -Infinity (NaN)
+F1 Score:           0.951327 (0.0003)
+AUPRC:              0.991949 (0.0025)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt
new file mode 100644
index 0000000000..45154c87f5
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt
@@ -0,0 +1,4 @@
+LinearSVM
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	Infinity	-Infinity	0.951327	0.991949	0.03	100	LinearSVM	%Data%		%Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	
+
diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt
new file mode 100644
index 0000000000..b337fd78da
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.PAVcalibration.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Probability	Log-loss	Assigned
+5	1	2.5100162	1	0	1
+6	0	-0.42988503	0.5	1	0
+8	0	-1.39884758	1E-15	1.4415419267167138E-15	0
+9	0	-1.20900154	1E-15	1.4415419267167138E-15	0
+10	0	-1.48176765	1E-15	1.4415419267167138E-15	0
+11	0	-1.41764426	1E-15	1.4415419267167138E-15	0
+18	1	1.4347012	1	0	1
+20	1	1.1132617	1	0	1
+21	1	1.42924047	1	0	1
+25	1	0.161359549	0.8666667	0.20645086423799175	1
+28	0	-1.41764426	1E-15	1.4415419267167138E-15	0
+31	0	-1.36339951	1E-15	1.4415419267167138E-15	0
+32	1	1.25826859	1	0	1
+35	0	-1.41764426	1E-15	1.4415419267167138E-15	0
+37	0	-0.835641861	1E-15	1.4415419267167138E-15	0
+40	0				
+41	1	0.227130175	0.8666667	0.20645086423799175	1
+44	1	1.61423349	1	0	1
+45	0	-1.47288513	1E-15	1.4415419267167138E-15	0
+46	1	1.08742118	1	0	1
+48	0	-1.118499	1E-15	1.4415419267167138E-15	0
+50	1	0.137539148	0.8666667	0.20645086423799175	1
+51	1	-0.2256087	0.714285731	0.48542679277371198	0
+52	1	0.825196	1	0	1
+54	1	1.26949811	1	0	1
+56	1	1.32781029	1	0	1
+60	1	0.291453838	0.8666667	0.20645086423799175	1
+63	1	-0.03475213	0.8666667	0.20645086423799175	0
+64	0	-1.49114561	1E-15	1.4415419267167138E-15	0
+66	0	-1.216397	1E-15	1.4415419267167138E-15	0
+68	1	1.63172913	1	0	1
+69	0	-1.38043249	1E-15	1.4415419267167138E-15	0
+70	0	-1.05505753	1E-15	1.4415419267167138E-15	0
+71	1	1.14647889	1	0	1
+72	0	-0.8491405	1E-15	1.4415419267167138E-15	0
+73	1	1.69674277	1	0	1
+74	1	0.2194643	0.8666667	0.20645086423799175	1
+76	0	-1.27802086	1E-15	1.4415419267167138E-15	0
+77	0	-1.024911	1E-15	1.4415419267167138E-15	0
+79	0	-1.37277746	1E-15	1.4415419267167138E-15	0
+82	0	-1.08125556	1E-15	1.4415419267167138E-15	0
+88	0	-1.216397	1E-15	1.4415419267167138E-15	0
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+96	0	-1.42294014	1E-15	1.4415419267167138E-15	0
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+98	1	1.55682516	1	0	1
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+100	1	0.96502924	1	0	1
+102	0	-1.14535546	1E-15	1.4415419267167138E-15	0
+104	1	2.01254773	1	0	1
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+111	1	0.72655797	0.8666667	0.20645086423799175	1
+112	1	1.30396724	1	0	1
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+121	0	-1.04286611	1E-15	1.4415419267167138E-15	0
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+123	1	0.7621949	1	0	1
+125	0	-1.49114561	1E-15	1.4415419267167138E-15	0
+128	1	0.9656298	1	0	1
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+132	1	1.94477773	1	0	1
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+137	0	-1.39838791	1E-15	1.4415419267167138E-15	0
+138	0	-1.19960022	1E-15	1.4415419267167138E-15	0
+141	0	-1.47188914	1E-15	1.4415419267167138E-15	0
+144	0	-1.41764426	1E-15	1.4415419267167138E-15	0
+145	0				
+147	0	-1.334388	1E-15	1.4415419267167138E-15	0
+150	0	-1.48176765	1E-15	1.4415419267167138E-15	0
+151	1	0.7797704	1	0	1
+152	1	1.83814359	1	0	1
+154	0	-1.54539037	1E-15	1.4415419267167138E-15	0
+156	0	-1.35846376	1E-15	1.4415419267167138E-15	0
+161	0	-1.1599288	1E-15	1.4415419267167138E-15	0
+164	0				
+167	1	1.07981849	1	0	1
+169	0	-1.5413084	1E-15	1.4415419267167138E-15	0
+171	0	-1.43690062	1E-15	1.4415419267167138E-15	0
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+191	1	2.29551339	1	0	1
+192	0	-1.19714069	1E-15	1.4415419267167138E-15	0
+196	0	1.06304407	1	Infinity	1
+198	0	-1.54539037	1E-15	1.4415419267167138E-15	0
+199	0	-1.34414315	1E-15	1.4415419267167138E-15	0
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+216	0	-1.49114561	1E-15	1.4415419267167138E-15	0
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diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-out.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-out.txt
new file mode 100644
index 0000000000..ab78c00f64
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-out.txt
@@ -0,0 +1,58 @@
+maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali={} dout=%Output% data=%Data% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
+Not training a calibrator because a valid calibrator trainer was not provided.
+Warning: Data does not contain a probability column. Will not output the Log-loss column
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter)
+Not training a calibrator because a valid calibrator trainer was not provided.
+Warning: Data does not contain a probability column. Will not output the Log-loss column
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      128 |        6 | 0.9552
+ negative ||        7 |      213 | 0.9682
+          ||======================
+Precision ||   0.9481 |   0.9726 |
+OVERALL 0/1 ACCURACY: 0.963277
+LOG LOSS/instance:      NaN
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): 0.000000
+AUC:                0.994233
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||       97 |        8 | 0.9238
+ negative ||        2 |      222 | 0.9911
+          ||======================
+Precision ||   0.9798 |   0.9652 |
+OVERALL 0/1 ACCURACY: 0.969605
+LOG LOSS/instance:      NaN
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.000000
+AUC:                0.997491
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995862 (0.0016)
+Accuracy:           0.966441 (0.0032)
+Positive precision: 0.963973 (0.0158)
+Positive recall:    0.939517 (0.0157)
+Negative precision: 0.968910 (0.0037)
+Negative recall:    0.979627 (0.0114)
+Log-loss:               NaN (NaN)
+Log-loss reduction: 0.000000 (0.0000)
+F1 Score:           0.951327 (0.0003)
+AUPRC:              0.991949 (0.0025)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-rp.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-rp.txt
new file mode 100644
index 0000000000..b12b8ce3c0
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration-rp.txt
@@ -0,0 +1,4 @@
+LinearSVM
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.995862	0.966441	0.963973	0.939517	0.96891	0.979627	NaN	0	0.951327	0.991949	0.03	100	LinearSVM	%Data%		%Output%	99	0	0	maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali={} dout=%Output% data=%Data% seed=1	/lambda:0.03;/iter:100	
+
diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt
new file mode 100644
index 0000000000..fed143de3d
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-CV-breast-cancer.nocalibration.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Assigned
+5	1	2.5100162	1
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diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt
new file mode 100644
index 0000000000..8648210735
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt
@@ -0,0 +1,39 @@
+maml.exe TrainTest test=%Data% tr=LinearSVM{iter=100 lambda=0.03} cali=PAV dout=%Output% data=%Data% out=%Output% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter)
+Training calibrator.
+PAV calibrator:  piecewise function approximation has 8 components.
+Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3499 (239.0/(239.0+444.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      226 |       13 | 0.9456
+ negative ||        9 |      435 | 0.9797
+          ||======================
+Precision ||   0.9617 |   0.9710 |
+OVERALL 0/1 ACCURACY: 0.967789
+LOG LOSS/instance:  0.084588
+Test-set entropy (prior Log-Loss/instance): 0.934003
+LOG-LOSS REDUCTION (RIG): 90.943543
+AUC:                0.995797
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995797 (0.0000)
+Accuracy:           0.967789 (0.0000)
+Positive precision: 0.961702 (0.0000)
+Positive recall:    0.945607 (0.0000)
+Negative precision: 0.970982 (0.0000)
+Negative recall:    0.979730 (0.0000)
+Log-loss:           0.084588 (0.0000)
+Log-loss reduction: 90.943543 (0.0000)
+F1 Score:           0.953586 (0.0000)
+AUPRC:              0.991453 (0.0000)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt
new file mode 100644
index 0000000000..2f6bd4df6f
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt
@@ -0,0 +1,4 @@
+LinearSVM
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.995797	0.967789	0.961702	0.945607	0.970982	0.97973	0.084588	90.94354	0.953586	0.991453	0.03	100	LinearSVM	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LinearSVM{iter=100 lambda=0.03} cali=PAV dout=%Output% data=%Data% out=%Output% seed=1	/lambda:0.03;/iter:100	
+
diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt
new file mode 100644
index 0000000000..9c673bc10a
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Probability	Log-loss	Assigned
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+1	0	0.7545147	0.826086938	2.5235618055722013	1
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diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt
new file mode 100644
index 0000000000..20ae32f902
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt
@@ -0,0 +1,39 @@
+maml.exe TrainTest test=%Data% tr=LinearSVM{iter=100 lambda=0.03} cali={} dout=%Output% data=%Data% out=%Output% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter)
+Not training a calibrator because a valid calibrator trainer was not provided.
+Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3499 (239.0/(239.0+444.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      226 |       13 | 0.9456
+ negative ||        9 |      435 | 0.9797
+          ||======================
+Precision ||   0.9617 |   0.9710 |
+OVERALL 0/1 ACCURACY: 0.967789
+LOG LOSS/instance:      NaN
+Test-set entropy (prior Log-Loss/instance): 0.934003
+LOG-LOSS REDUCTION (RIG): 0.000000
+AUC:                0.995797
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.995797 (0.0000)
+Accuracy:           0.967789 (0.0000)
+Positive precision: 0.961702 (0.0000)
+Positive recall:    0.945607 (0.0000)
+Negative precision: 0.970982 (0.0000)
+Negative recall:    0.979730 (0.0000)
+Log-loss:               NaN (0.0000)
+Log-loss reduction: 0.000000 (0.0000)
+F1 Score:           0.953586 (0.0000)
+AUPRC:              0.991453 (0.0000)
+
+---------------------------------------
+Warning: Data does not contain a probability column. Will not output the Log-loss column
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+
diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt
new file mode 100644
index 0000000000..4395e7b3b5
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt
@@ -0,0 +1,4 @@
+LinearSVM
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/lambda	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.995797	0.967789	0.961702	0.945607	0.970982	0.97973	NaN	0	0.953586	0.991453	0.03	100	LinearSVM	%Data%	%Data%	%Output%	99	0	0	maml.exe TrainTest test=%Data% tr=LinearSVM{iter=100 lambda=0.03} cali={} dout=%Output% data=%Data% out=%Output% seed=1	/lambda:0.03;/iter:100	
+
diff --git a/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt
new file mode 100644
index 0000000000..c5e41e5240
--- /dev/null
+++ b/ZBaselines/SingleRelease/LinearSVM/LinearSVM-TrainTest-breast-cancer.nocalibration.txt
@@ -0,0 +1,700 @@
+Instance	Label	Score	Assigned
+0	0	-1.092935	0
+1	0	0.7545147	1
+2	0	-1.13008308	0
+3	0	0.631343842	1
+4	0	-1.07333851	0
+5	1	2.51247835	1
+6	0	-0.391096115	0
+7	0	-1.24698257	0
+8	0	-1.34032881	0
+9	0	-1.15489388	0
+10	0	-1.43061268	0
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+12	1	-0.2854843	0
+13	0	-1.1672312	0
+14	1	1.69779134	1
+15	1	0.06286478	1
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+20	1	1.133441	1
+21	1	1.46698761	1
+22	0	-1.29266214	0
+23	1		
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+32	1	1.277215	1
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+36	1	1.68721437	1
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diff --git a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
index 4a93d3468d..95e2374eb2 100644
--- a/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
+++ b/test/Microsoft.ML.Predictor.Tests/TestPredictors.cs
@@ -1058,21 +1058,26 @@ public void CalibratorPerceptronTest()
         }
 
         /// <summary>
-        ///A test for calibrators
+        ///A test for default calibrators
         ///</summary>
-        [Fact(Skip = "Need CoreTLC specific baseline update")]
+        [Fact]
         [TestCategory("Calibrator")]
-        public void CalibratorLinearSvmTest()
+        public void NoCalibratorLinearSvmTest()
         {
             var datasets = GetDatasetsForCalibratorTest();
-            RunAllTests(
-                new[] { TestLearners.linearSVM },
-                datasets,
-                new string[] { "cali={}" }, "nocalibration");
-            RunAllTests(
-                new[] { TestLearners.linearSVM },
-                datasets,
-                new string[] { "cali=PAV" }, "PAVcalibration");
+            RunAllTests( new[] { TestLearners.linearSVM }, datasets, new string[] { "cali={}" }, "nocalibration");
+            Done();
+        }
+
+        /// <summary>
+        ///A test for PAV calibrators
+        ///</summary>
+        [Fact]
+        [TestCategory("Calibrator")]
+        public void PAVCalibratorLinearSvmTest()
+        {
+            var datasets = GetDatasetsForCalibratorTest();
+            RunAllTests( new[] { TestLearners.linearSVM }, datasets, new string[] { "cali=PAV" }, "PAVcalibration");
             Done();
         }