scikit-learn
diff --git a/‎doc/modules/preprocessing.rst
Lines changed: 11 additions & 11 deletions b/‎doc/modules/preprocessing.rst
Lines changed: 11 additions & 11 deletions
diff --git a/‎sklearn/pipeline.py
Lines changed: 4 additions & 3 deletions b/‎sklearn/pipeline.py
Lines changed: 4 additions & 3 deletions
@@ -72,13 +72,14 @@ This class is hence suitable for use in the early steps of a
 :class:`sklearn.pipeline.Pipeline`::
 
   >>> scaler = preprocessing.StandardScaler().fit(X)
-  >>> scaler
-  StandardScaler(copy=True, with_mean=True, with_std=True)
+  >>> scaler                                        # doctest: +NORMALIZE_WHITESPACE
+  StandardScaler(axis=0, copy=True, with_centering=True, with_mean=None,
+            with_scaling=True, with_std=None)
 
-  >>> scaler.mean_                                      # doctest: +ELLIPSIS
+  >>> scaler.center_                                    # doctest: +ELLIPSIS
   array([ 1. ...,  0. ...,  0.33...])
 
-  >>> scaler.std_                                       # doctest: +ELLIPSIS
+  >>> scaler.scale_                                     # doctest: +ELLIPSIS
   array([ 0.81...,  0.81...,  1.24...])
 
   >>> scaler.transform(X)                               # doctest: +ELLIPSIS
@@ -94,7 +95,7 @@ same way it did on the training set::
   array([[-2.44...,  1.22..., -0.26...]])
 
 It is possible to disable either centering or scaling by either
-passing ``with_mean=False`` or ``with_std=False`` to the constructor
+passing ``with_centering=False`` or ``with_scaling=False`` to the constructor
 of :class:`StandardScaler`.
 
 
@@ -132,11 +133,11 @@ applied to be consistent with the transformation performed on the train data::
 It is possible to introspect the scaler attributes to find about the exact
 nature of the transformation learned on the training data::
 
-  >>> min_max_scaler.scale_                             # doctest: +ELLIPSIS
-  array([ 0.5       ,  0.5       ,  0.33...])
+  >>> min_max_scaler.scale_                             # doctest: +NORMALIZE_WHITESPACE
+  array([ 2.,  2.,  3.])
 
-  >>> min_max_scaler.min_                               # doctest: +ELLIPSIS
-  array([ 0.        ,  0.5       ,  0.33...])
+  >>> min_max_scaler.center_                            # doctest: +ELLIPSIS
+  array([ 0., -1., -1.])
 
 If :class:`MinMaxScaler` is given an explicit ``feature_range=(min, max)`` the
 full formula is::
@@ -526,6 +527,5 @@ similarly.
 
    Note that if features have very different scaling or statistical
    properties, :class:`cluster.FeatureAgglomeration` maye not be able to
-   capture the links between related features. Using a 
+   capture the links between related features. Using a
    :class:`preprocessing.StandardScaler` can be useful in these settings.
-
@@ -237,10 +237,11 @@ def make_pipeline(*steps):
     --------
     >>> from sklearn.naive_bayes import GaussianNB
     >>> from sklearn.preprocessing import StandardScaler
+    >>> from sklearn.pipeline import make_pipeline
     >>> make_pipeline(StandardScaler(), GaussianNB())    # doctest: +NORMALIZE_WHITESPACE
-    Pipeline(steps=[('standardscaler',
-                     StandardScaler(copy=True, with_mean=True, with_std=True)),
-                    ('gaussiannb', GaussianNB())])
+        Pipeline(steps=[('standardscaler', StandardScaler(axis=0, copy=True,
+                 with_centering=True, with_mean=None, with_scaling=True,
+                 with_std=None)), ('gaussiannb', GaussianNB())])
 
     Returns
     -------