DOC show usage of __ in Pipeline and FeatureUnion (scikit-learn#26661)

Author: Charlie-XIAO

sklearn/pipeline.py

@@ -131,10 +131,11 @@ class Pipeline(_BaseComposition):
     >>> pipe = Pipeline([('scaler', StandardScaler()), ('svc', SVC())])
     >>> # The pipeline can be used as any other estimator
     >>> # and avoids leaking the test set into the train set
-    >>> pipe.fit(X_train, y_train)
-    Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())])
-    >>> pipe.score(X_test, y_test)
+    >>> pipe.fit(X_train, y_train).score(X_test, y_test)
     0.88
+    >>> # An estimator's parameter can be set using '__' syntax
+    >>> pipe.set_params(svc__C=10).fit(X_train, y_train).score(X_test, y_test)
+    0.76
     """
 
     # BaseEstimator interface
@@ -1051,6 +1052,10 @@ class FeatureUnion(TransformerMixin, _BaseComposition):
     >>> union.fit_transform(X)
     array([[ 1.5       ,  3.0...,  0.8...],
            [-1.5       ,  5.7..., -0.4...]])
+    >>> # An estimator's parameter can be set using '__' syntax
+    >>> union.set_params(pca__n_components=1).fit_transform(X)
+    array([[ 1.5       ,  3.0...],
+           [-1.5       ,  5.7...]])
     """
 
     _required_parameters = ["transformer_list"]
@@ -1362,11 +1367,12 @@ def __getitem__(self, name):
 
 
 def make_union(*transformers, n_jobs=None, verbose=False):
-    """Construct a FeatureUnion from the given transformers.
+    """Construct a :class:`FeatureUnion` from the given transformers.
 
-    This is a shorthand for the FeatureUnion constructor; it does not require,
-    and does not permit, naming the transformers. Instead, they will be given
-    names automatically based on their types. It also does not allow weighting.
+    This is a shorthand for the :class:`FeatureUnion` constructor; it does not
+    require, and does not permit, naming the transformers. Instead, they will
+    be given names automatically based on their types. It also does not allow
+    weighting.
 
     Parameters
     ----------