scikit-learn
diff --git a/‎doc/modules/compose.rst
Copy file name to clipboardExpand all lines: doc/modules/compose.rst
+1-1Lines changed: 1 addition & 1 deletion b/‎doc/modules/compose.rst
Copy file name to clipboardExpand all lines: doc/modules/compose.rst
+1-1Lines changed: 1 addition & 1 deletion
diff --git a/‎sklearn/pipeline.py
Copy file name to clipboardExpand all lines: sklearn/pipeline.py
+14-14Lines changed: 14 additions & 14 deletions b/‎sklearn/pipeline.py
Copy file name to clipboardExpand all lines: sklearn/pipeline.py
+14-14Lines changed: 14 additions & 14 deletions
@@ -198,7 +198,7 @@ after calling ``fit``.
 This feature is used to avoid computing the fit transformers within a pipeline
 if the parameters and input data are identical. A typical example is the case of
 a grid search in which the transformers can be fitted only once and reused for
-each configuration.
+each configuration. The last step will never be cached, even if it is a transformer.
 
 The parameter ``memory`` is needed in order to cache the transformers.
 ``memory`` can be either a string containing the directory where to cache the
 
@@ -80,13 +80,13 @@ class Pipeline(_BaseComposition):
         estimator.
 
     memory : str or object with the joblib.Memory interface, default=None
-        Used to cache the fitted transformers of the pipeline. By default,
-        no caching is performed. If a string is given, it is the path to
-        the caching directory. Enabling caching triggers a clone of
-        the transformers before fitting. Therefore, the transformer
-        instance given to the pipeline cannot be inspected
-        directly. Use the attribute ``named_steps`` or ``steps`` to
-        inspect estimators within the pipeline. Caching the
+        Used to cache the fitted transformers of the pipeline. The last step
+        will never be cached, even if it is a transformer. By default, no
+        caching is performed. If a string is given, it is the path to the
+        caching directory. Enabling caching triggers a clone of the transformers
+        before fitting. Therefore, the transformer instance given to the
+        pipeline cannot be inspected directly. Use the attribute ``named_steps``
+        or ``steps`` to inspect estimators within the pipeline. Caching the
         transformers is advantageous when fitting is time consuming.
 
     verbose : bool, default=False
@@ -858,13 +858,13 @@ def make_pipeline(*steps, memory=None, verbose=False):
         List of the scikit-learn estimators that are chained together.
 
     memory : str or object with the joblib.Memory interface, default=None
-        Used to cache the fitted transformers of the pipeline. By default,
-        no caching is performed. If a string is given, it is the path to
-        the caching directory. Enabling caching triggers a clone of
-        the transformers before fitting. Therefore, the transformer
-        instance given to the pipeline cannot be inspected
-        directly. Use the attribute ``named_steps`` or ``steps`` to
-        inspect estimators within the pipeline. Caching the
+        Used to cache the fitted transformers of the pipeline. The last step
+        will never be cached, even if it is a transformer. By default, no
+        caching is performed. If a string is given, it is the path to the
+        caching directory. Enabling caching triggers a clone of the transformers
+        before fitting. Therefore, the transformer instance given to the
+        pipeline cannot be inspected directly. Use the attribute ``named_steps``
+        or ``steps`` to inspect estimators within the pipeline. Caching the
         transformers is advantageous when fitting is time consuming.
 
     verbose : bool, default=False