Removed "TODO: Replace with CV" comments

src/python/nimbusml/examples/AveragedPerceptronBinaryClassifier.py

@@ -20,7 +20,6 @@
     feature=['age', 'parity', 'spontaneous'], label='case')])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/FactorizationMachineBinaryClassifier.py

@@ -26,7 +26,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/FastForestBinaryClassifier.py

@@ -25,7 +25,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/FastLinearBinaryClassifier.py

@@ -23,7 +23,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/FastLinearClassifier.py

@@ -24,7 +24,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/FastLinearRegressor.py

@@ -23,7 +23,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/FastTreesBinaryClassifier.py

@@ -23,7 +23,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/FastTreesRegressor.py

@@ -23,7 +23,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/FastTreesTweedieRegressor.py

@@ -23,7 +23,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/GamBinaryClassifier.py

@@ -23,7 +23,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/GamRegressor.py

@@ -23,7 +23,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/KMeansPlusPlus.py

@@ -24,7 +24,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline \
     .fit(data) \
     .test(data, 'induced', output_scores=True)

src/python/nimbusml/examples/LightGbmBinaryClassifier.py

@@ -26,7 +26,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(
     data, 'case').test(
     data, output_scores=True)

src/python/nimbusml/examples/LightGbmClassifier.py

@@ -26,7 +26,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/LightGbmRanker.py

@@ -16,7 +16,6 @@
     feature=['Class', 'dep_day', 'duration'], label='rank', group_id='group')])
 
 # train, predict, and evaluate.
-# TODO: Replace with CV
 metrics, predictions = pipeline \
     .fit(data) \
     .test(data, output_scores=True)

src/python/nimbusml/examples/LightGbmRegressor.py

@@ -26,7 +26,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/LinearSvmBinaryClassifier.py

@@ -20,7 +20,6 @@
     feature=['age', 'parity', 'spontaneous'], label='case')])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/LogisticRegressionBinaryClassifier.py

@@ -24,7 +24,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/LogisticRegressionClassifier.py

@@ -24,7 +24,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/NaiveBayesClassifier.py

@@ -25,7 +25,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/OneVsRestClassifier.py

@@ -30,7 +30,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/OnlineGradientDescentRegressor.py

@@ -24,7 +24,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/OrdinaryLeastSquaresRegressor.py

@@ -24,7 +24,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/PcaAnomalyDetector.py

@@ -24,7 +24,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(
     data, 'case', output_scores=True)
 #      Score

src/python/nimbusml/examples/PoissonRegressionRegressor.py

@@ -24,7 +24,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/SgdBinaryClassifier.py

@@ -24,7 +24,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions

src/python/nimbusml/examples/SymSgdBinaryClassifier.py

@@ -24,7 +24,6 @@
 ])
 
 # train, predict, and evaluate
-# TODO: Replace with CV
 metrics, predictions = pipeline.fit(data).test(data, output_scores=True)
 
 # print predictions