Revert "DOC More details about the attributes in MinMaxScaler (scikit-learn#13029)"

Xing · Xing · commit 710c54bed066 · 2019-04-28T15:16:38.000-04:00
This reverts commit 65435bf.
diff --git a/sklearn/preprocessing/data.py b/sklearn/preprocessing/data.py
@@ -210,11 +210,6 @@ class MinMaxScaler(BaseEstimator, TransformerMixin):
 
     where min, max = feature_range.
 
-    The transformation is calculated as::
-
-        X_scaled = scale * X + min - X.min(axis=0) * scale
-        where scale = (max - min) / (X.max(axis=0) - X.min(axis=0))
-
     This transformation is often used as an alternative to zero mean,
     unit variance scaling.
 
@@ -232,12 +227,10 @@ class MinMaxScaler(BaseEstimator, TransformerMixin):
     Attributes
     ----------
     min_ : ndarray, shape (n_features,)
-        Per feature adjustment for minimum. Equivalent to
-        ``min - X.min(axis=0) * self.scale_``
+        Per feature adjustment for minimum.
 
     scale_ : ndarray, shape (n_features,)
-        Per feature relative scaling of the data. Equivalent to
-        ``(max - min) / (X.max(axis=0) - X.min(axis=0))``
+        Per feature relative scaling of the data.
 
         .. versionadded:: 0.17
            *scale_* attribute.
@@ -416,17 +409,12 @@ def minmax_scale(X, feature_range=(0, 1), axis=0, copy=True):
     that it is in the given range on the training set, i.e. between
     zero and one.
 
-    The transformation is given by (when ``axis=0``)::
+    The transformation is given by::
 
         X_std = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
         X_scaled = X_std * (max - min) + min
 
     where min, max = feature_range.
- 
-    The transformation is calculated as (when ``axis=0``)::
-
-       X_scaled = scale * X + min - X.min(axis=0) * scale
-       where scale = (max - min) / (X.max(axis=0) - X.min(axis=0))
 
     This transformation is often used as an alternative to zero mean,
     unit variance scaling.
