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[MRG+1] Docs: refer users to the other encoders to do one hot encoding for labels. #7315

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Oct 5, 2016
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[MRG+1] Docs: refer users to the other encoders to do one hot encoding for labels. #7315

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f76f689
refer users to the other encoders to do one hot encoding for labels.
kathyxchen Aug 31, 2016
c7b0ea9
added to the 'see more' for labelbinarizer, multilabelbinarizer, and …
kathyxchen Sep 1, 2016
e657c79
added note about y labels to the OneHotEncoder docstring
kathyxchen Sep 1, 2016
b27cf06
removed example from MultiLabelBinarizer
Sep 17, 2016
52bc72e
documentation should specify LabelBinarizer, not MultiLabelBinarizer …
kathyxchen Sep 21, 2016
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10 changes: 10 additions & 0 deletions sklearn/preprocessing/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1737,6 +1737,9 @@ class OneHotEncoder(BaseEstimator, TransformerMixin):
This encoding is needed for feeding categorical data to many scikit-learn
estimators, notably linear models and SVMs with the standard kernels.

Note: a one-hot encoding of y labels should use a LabelBinarizer
instead.

Read more in the :ref:`User Guide <preprocessing_categorical_features>`.

Parameters
Expand Down Expand Up @@ -1810,6 +1813,13 @@ class OneHotEncoder(BaseEstimator, TransformerMixin):
dictionary items (also handles string-valued features).
sklearn.feature_extraction.FeatureHasher : performs an approximate one-hot
encoding of dictionary items or strings.
sklearn.preprocessing.LabelBinarizer : binarizes labels in a one-vs-all
fashion.
sklearn.preprocessing.MultiLabelBinarizer : transforms between iterable of
iterables and a multilabel format, e.g. a (samples x classes) binary
matrix indicating the presence of a class label.
sklearn.preprocessing.LabelEncoder : encodes labels with values between 0
and n_classes-1.
"""
def __init__(self, n_values="auto", categorical_features="all",
dtype=np.float64, sparse=True, handle_unknown='error'):
Expand Down
11 changes: 11 additions & 0 deletions sklearn/preprocessing/label.py
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Expand Up @@ -91,6 +91,10 @@ class LabelEncoder(BaseEstimator, TransformerMixin):
>>> list(le.inverse_transform([2, 2, 1]))
['tokyo', 'tokyo', 'paris']

See also
--------
sklearn.preprocessing.OneHotEncoder : encode categorical integer features
using a one-hot aka one-of-K scheme.
"""

def fit(self, y):
Expand Down Expand Up @@ -257,6 +261,8 @@ class LabelBinarizer(BaseEstimator, TransformerMixin):
--------
label_binarize : function to perform the transform operation of
LabelBinarizer with fixed classes.
sklearn.preprocessing.OneHotEncoder : encode categorical integer features
using a one-hot aka one-of-K scheme.
"""

def __init__(self, neg_label=0, pos_label=1, sparse_output=False):
Expand Down Expand Up @@ -648,6 +654,7 @@ class MultiLabelBinarizer(BaseEstimator, TransformerMixin):

Examples
--------
>>> from sklearn.preprocessing import MultiLabelBinarizer
>>> mlb = MultiLabelBinarizer()
>>> mlb.fit_transform([(1, 2), (3,)])
array([[1, 1, 0],
Expand All @@ -661,6 +668,10 @@ class MultiLabelBinarizer(BaseEstimator, TransformerMixin):
>>> list(mlb.classes_)
['comedy', 'sci-fi', 'thriller']

See also
--------
sklearn.preprocessing.OneHotEncoder : encode categorical integer features
using a one-hot aka one-of-K scheme.
"""
def __init__(self, classes=None, sparse_output=False):
self.classes = classes
Expand Down