DOC: Correct confusing docs of n_values in OneHotEncoder

Author: MechCoder

doc/modules/preprocessing.rst

@@ -411,6 +411,18 @@ Then we fit the estimator, and transform a data point.
 In the result, the first two numbers encode the gender, the next set of three
 numbers the continent and the last four the web browser.
 
+Note that, if there is a possibilty that the training data might have missing categorical
+features, one has to explicitly set ``n_values``. For example,
+
+ >>> enc = preprocessing.OneHotEncoder(n_values=[2, 3, 4])
+ >>> # Note that for there are missing categorical values for the 2nd and 3rd
+ >>> # feature
+ >>> enc.fit([[1, 2, 3], [0, 2, 0]]) # doctest: +ELLIPSIS
+ OneHotEncoder(categorical_features='all', dtype=<... 'numpy.float64'>,
+ handle_unknown='error', n_values=[2, 3, 4], sparse=True)
+ >>> enc.transform([[1, 0, 0]]).toarray()
+ array([[ 0., 1., 1., 0., 0., 1., 0., 0., 0.]])
+
 See :ref:`dict_feature_extraction` for categorical features that are represented
 as a dict, not as integers.
 

sklearn/preprocessing/data.py

@@ -1650,8 +1650,10 @@ class OneHotEncoder(BaseEstimator, TransformerMixin):
  Number of values per feature.
 
  - 'auto' : determine value range from training data.
- - int : maximum value for all features.
- - array : maximum value per feature.
+ - int : number of categorical values per feature.
+ Each feature value should be in ``range(n_values)``
+ - array : ``n_values[i]`` is the number of categorical values in
+ ``X[:, i]``. Each feature value should be in ``range(n_values[i])``
 
  categorical_features: "all" or array of indices or mask
  Specify what features are treated as categorical.