Docs update

src/python/docs/docstrings/ClassifierBestPerformanceSelector.txt

@@ -32,7 +32,4 @@
 
     .. index:: models, ensemble, classification
 
-    Example:
-       .. literalinclude:: /../nimbusml/examples/EnsembleClassifier.py
-              :language: python
     """

src/python/docs/docstrings/EnsembleClassifier.txt

@@ -51,7 +51,7 @@
 		  or ``"LogLossReduction"``.
 
 
-	:output_combiner: indicates how to combine the predictions of the different
+	:param output_combiner: indicates how to combine the predictions of the different
 	    models into a single prediction. There are five available output
 		combiners for clasification:
 

src/python/docs/docstrings/EnsembleRegressor.txt

@@ -51,7 +51,7 @@
 		  ``"RSquared"``.
 
 
-	:output_combiner: indicates how to combine the predictions of the different
+	:param output_combiner: indicates how to combine the predictions of the different
 	    models into a single prediction. There are five available output
 		combiners for clasification:
 
@@ -126,7 +126,7 @@
         <nimbusml.ensemble.output_combiner.RegressorStacking>`
 
 
-    .. index:: models, ensemble, classification
+    .. index:: models, ensemble, regression
 
     Example:
        .. literalinclude:: /../nimbusml/examples/EnsembleRegressor.py

src/python/docs/docstrings/LinearSvmBinaryClassifier.txt

@@ -5,11 +5,11 @@
     .. remarks::
         Linear SVM implements an algorithm that finds a hyperplane in the
 		feature space for binary classification, by solving an SVM problem.
-		For instance, with feature values *f_0, f_1,..., f_{D-1}*, the
+		For instance, with feature values $f_0, f_1,..., f_{D-1}$, the
 		prediction is given by determining what side of the hyperplane the
 		point falls into. That is the same as the sign of the feautures'
-		weighted sum, i.e. *\sum_{i = 0}^{D-1} \left(w_i * f_i \right) + b*,
-		where *w_0, w_1,..., w_{D-1}* are the weights computed by the
+		weighted sum, i.e. $\sum_{i = 0}^{D-1} \left(w_i * f_i \right) + b$,
+		where $w_0, w_1,..., w_{D-1}$ are the weights computed by the
 		algorithm, and *b* is the bias computed by the algorithm.
 
 		This algorithm implemented is the PEGASOS method, which alternates

src/python/docs/docstrings/RegressorBestPerformanceSelector.txt

@@ -28,9 +28,6 @@
         <nimbusml.ensemble.output_combiner.RegressorStacking>`
 
 
-    .. index:: models, ensemble, classification
+    .. index:: models, ensemble, regression
 
-    Example:
-       .. literalinclude:: /../nimbusml/examples/EnsembleClassifier.py
-              :language: python
     """

src/python/nimbusml/ensemble/ensembleclassifier.py

@@ -77,8 +77,7 @@ class EnsembleClassifier(core, BasePredictor, ClassifierMixin):
           ``"AccuracyMicro"``, ``"AccuracyMacro"``,    ``"LogLoss"``,
           or ``"LogLossReduction"``.
 
-
-    :output_combiner: indicates how to combine the predictions of the different
+    :param output_combiner: indicates how to combine the predictions of the different
         models into a single prediction. There are five available output
         combiners for clasification:
 
@@ -97,8 +96,6 @@ class EnsembleClassifier(core, BasePredictor, ClassifierMixin):
         outputs of the trained models, weighted by the specified metric. The
           metric can be ``"AccuracyMicroAvg"`` or ``"AccuracyMacroAvg"``.
 
-    :param output_combiner: Output combiner.
-
     :param normalize: Specifies the type of automatic normalization used:
 
         * ``"Auto"``: if normalization is needed, it is performed

src/python/nimbusml/ensemble/ensembleregressor.py

@@ -77,8 +77,7 @@ class EnsembleRegressor(core, BasePredictor, RegressorMixin):
           can be ``"L1"``, ``"L2"``, ``"Rms"``, or ``"Loss"``, or
           ``"RSquared"``.
 
-
-    :output_combiner: indicates how to combine the predictions of the different
+    :param output_combiner: indicates how to combine the predictions of the different
         models into a single prediction. There are five available output
         combiners for clasification:
 
@@ -91,8 +90,6 @@ class EnsembleRegressor(core, BasePredictor, RegressorMixin):
           of the different models on a training instance, and the instance's
           label.
 
-    :param output_combiner: Output combiner.
-
     :param normalize: Specifies the type of automatic normalization used:
 
         * ``"Auto"``: if normalization is needed, it is performed
@@ -166,7 +163,7 @@ class EnsembleRegressor(core, BasePredictor, RegressorMixin):
         <nimbusml.ensemble.output_combiner.RegressorStacking>`
 
 
-    .. index:: models, ensemble, classification
+    .. index:: models, ensemble, regression
 
     Example:
        .. literalinclude:: /../nimbusml/examples/EnsembleRegressor.py

src/python/nimbusml/ensemble/output_combiner/__init__.py

@@ -13,7 +13,7 @@
     'ClassifierStacking',
     'ClassifierVoting',
     'ClassifierWeightedAverage',
-    'ClassifierAverage',
-    'ClassifierMedian',
-    'ClassifierStacking'
+    'RegressorAverage',
+    'RegressorMedian',
+    'RegressorStacking'
 ]

src/python/nimbusml/ensemble/sub_model_selector/classifierbestperformanceselector.py

@@ -58,9 +58,6 @@ class ClassifierBestPerformanceSelector(core):
 
     .. index:: models, ensemble, classification
 
-    Example:
-       .. literalinclude:: /../nimbusml/examples/EnsembleClassifier.py
-              :language: python
     """
 
     @trace

src/python/nimbusml/ensemble/sub_model_selector/regressorbestperformanceselector.py

@@ -54,11 +54,8 @@ class RegressorBestPerformanceSelector(core):
         <nimbusml.ensemble.output_combiner.RegressorStacking>`
 
 
-    .. index:: models, ensemble, classification
+    .. index:: models, ensemble, regression
 
-    Example:
-       .. literalinclude:: /../nimbusml/examples/EnsembleClassifier.py
-              :language: python
     """
 
     @trace

src/python/nimbusml/internal/core/ensemble/ensembleclassifier.py

@@ -77,8 +77,7 @@ class EnsembleClassifier(
           ``"AccuracyMicro"``, ``"AccuracyMacro"``,    ``"LogLoss"``,
           or ``"LogLossReduction"``.
 
-
-    :output_combiner: indicates how to combine the predictions of the different
+    :param output_combiner: indicates how to combine the predictions of the different
         models into a single prediction. There are five available output
         combiners for clasification:
 
@@ -97,8 +96,6 @@ class EnsembleClassifier(
         outputs of the trained models, weighted by the specified metric. The
           metric can be ``"AccuracyMicroAvg"`` or ``"AccuracyMacroAvg"``.
 
-    :param output_combiner: Output combiner.
-
     :param normalize: Specifies the type of automatic normalization used:
 
         * ``"Auto"``: if normalization is needed, it is performed

src/python/nimbusml/internal/core/ensemble/ensembleregressor.py

@@ -75,8 +75,7 @@ class EnsembleRegressor(
           can be ``"L1"``, ``"L2"``, ``"Rms"``, or ``"Loss"``, or
           ``"RSquared"``.
 
-
-    :output_combiner: indicates how to combine the predictions of the different
+    :param output_combiner: indicates how to combine the predictions of the different
         models into a single prediction. There are five available output
         combiners for clasification:
 
@@ -89,8 +88,6 @@ class EnsembleRegressor(
           of the different models on a training instance, and the instance's
           label.
 
-    :param output_combiner: Output combiner.
-
     :param normalize: Specifies the type of automatic normalization used:
 
         * ``"Auto"``: if normalization is needed, it is performed
@@ -164,7 +161,7 @@ class EnsembleRegressor(
         <nimbusml.ensemble.output_combiner.RegressorStacking>`
 
 
-    .. index:: models, ensemble, classification
+    .. index:: models, ensemble, regression
 
     Example:
        .. literalinclude:: /../nimbusml/examples/EnsembleRegressor.py

src/python/nimbusml/internal/core/ensemble/sub_model_selector/classifierbestperformanceselector.py

@@ -58,9 +58,6 @@ class ClassifierBestPerformanceSelector(Component):
 
     .. index:: models, ensemble, classification
 
-    Example:
-       .. literalinclude:: /../nimbusml/examples/EnsembleClassifier.py
-              :language: python
     """
 
     @trace

src/python/nimbusml/internal/core/ensemble/sub_model_selector/regressorbestperformanceselector.py

@@ -54,11 +54,8 @@ class RegressorBestPerformanceSelector(Component):
         <nimbusml.ensemble.output_combiner.RegressorStacking>`
 
 
-    .. index:: models, ensemble, classification
+    .. index:: models, ensemble, regression
 
-    Example:
-       .. literalinclude:: /../nimbusml/examples/EnsembleClassifier.py
-              :language: python
     """
 
     @trace

src/python/nimbusml/internal/core/linear_model/linearsvmbinaryclassifier.py

@@ -26,11 +26,11 @@ class LinearSvmBinaryClassifier(
     .. remarks::
         Linear SVM implements an algorithm that finds a hyperplane in the
         feature space for binary classification, by solving an SVM problem.
-        For instance, with feature values *f_0, f_1,..., f_{D-1}*, the
+        For instance, with feature values $f_0, f_1,..., f_{D-1}$, the
         prediction is given by determining what side of the hyperplane the
         point falls into. That is the same as the sign of the feautures'
-        weighted sum, i.e. *\sum_{i = 0}^{D-1} \left(w_i * f_i \right) + b*,
-        where *w_0, w_1,..., w_{D-1}* are the weights computed by the
+        weighted sum, i.e. $\sum_{i = 0}^{D-1} \left(w_i * f_i \right) + b$,
+        where $w_0, w_1,..., w_{D-1}$ are the weights computed by the
         algorithm, and *b* is the bias computed by the algorithm.
 
         This algorithm implemented is the PEGASOS method, which alternates

src/python/nimbusml/linear_model/linearsvmbinaryclassifier.py

@@ -29,11 +29,11 @@ class LinearSvmBinaryClassifier(
     .. remarks::
         Linear SVM implements an algorithm that finds a hyperplane in the
         feature space for binary classification, by solving an SVM problem.
-        For instance, with feature values *f_0, f_1,..., f_{D-1}*, the
+        For instance, with feature values $f_0, f_1,..., f_{D-1}$, the
         prediction is given by determining what side of the hyperplane the
         point falls into. That is the same as the sign of the feautures'
-        weighted sum, i.e. *\sum_{i = 0}^{D-1} \left(w_i * f_i \right) + b*,
-        where *w_0, w_1,..., w_{D-1}* are the weights computed by the
+        weighted sum, i.e. $\sum_{i = 0}^{D-1} \left(w_i * f_i \right) + b$,
+        where $w_0, w_1,..., w_{D-1}$ are the weights computed by the
         algorithm, and *b* is the bias computed by the algorithm.
 
         This algorithm implemented is the PEGASOS method, which alternates