DOC Added cross-referencing of factory functions with their class counterparts (scikit-learn#11168)

Author: jmwoloso

sklearn/compose/_column_transformer.py

@@ -106,6 +106,12 @@ class ColumnTransformer(_BaseComposition, TransformerMixin):
     in the `passthrough` keyword. Those columns specified with `passthrough`
     are added at the right to the output of the transformers.
 
+    See also
+    --------
+    sklearn.compose.make_column_transformer : convenience function for
+        combining the outputs of multiple transformer objects applied to
+        column subsets of the original feature space.
+
     Examples
     --------
     >>> from sklearn.compose import ColumnTransformer
@@ -596,6 +602,12 @@ def make_column_transformer(*transformers, **kwargs):
     -------
     ct : ColumnTransformer
 
+    See also
+    --------
+    sklearn.compose.ColumnTransformer : Class that allows combining the
+        outputs of multiple transformer objects used on column subsets
+        of the data into a single feature space.
+
     Examples
     --------
     >>> from sklearn.preprocessing import StandardScaler, CategoricalEncoder

sklearn/pipeline.py

@@ -68,6 +68,11 @@ class Pipeline(_BaseComposition):
         Read-only attribute to access any step parameter by user given name.
         Keys are step names and values are steps parameters.
 
+    See also
+    --------
+    sklearn.pipeline.make_pipeline : convenience function for simplified
+        pipeline construction.
+
     Examples
     --------
     >>> from sklearn import svm
@@ -549,6 +554,11 @@ def make_pipeline(*steps, **kwargs):
         inspect estimators within the pipeline. Caching the
         transformers is advantageous when fitting is time consuming.
 
+    See also
+    --------
+    sklearn.pipeline.Pipeline : Class for creating a pipeline of
+        transforms with a final estimator.
+
     Examples
     --------
     >>> from sklearn.naive_bayes import GaussianNB
@@ -625,6 +635,11 @@ class FeatureUnion(_BaseComposition, TransformerMixin):
         Multiplicative weights for features per transformer.
         Keys are transformer names, values the weights.
 
+    See also
+    --------
+    sklearn.pipeline.make_union : convenience function for simplified
+        feature union construction.
+
     Examples
     --------
     >>> from sklearn.pipeline import FeatureUnion
@@ -823,6 +838,11 @@ def make_union(*transformers, **kwargs):
     -------
     f : FeatureUnion
 
+    See also
+    --------
+    sklearn.pipeline.FeatureUnion : Class for concatenating the results
+        of multiple transformer objects.
+
     Examples
     --------
     >>> from sklearn.decomposition import PCA, TruncatedSVD