class sklearn.preprocessing.MultiLabelBinarizer(classes=None, sparse_output=False)
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Transform between iterable of iterables and a multilabel format
Although a list of sets or tuples is a very intuitive format for multilabel data, it is unwieldy to process. This transformer converts between this intuitive format and the supported multilabel format: a (samples x classes) binary matrix indicating the presence of a class label.
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See also
sklearn.preprocessing.OneHotEncoder
>>> from sklearn.preprocessing import MultiLabelBinarizer >>> mlb = MultiLabelBinarizer() >>> mlb.fit_transform([(1, 2), (3,)]) array([[1, 1, 0], [0, 0, 1]]) >>> mlb.classes_ array([1, 2, 3])
>>> mlb.fit_transform([set(['sci-fi', 'thriller']), set(['comedy'])]) array([[0, 1, 1], [1, 0, 0]]) >>> list(mlb.classes_) ['comedy', 'sci-fi', 'thriller']
fit (y) | Fit the label sets binarizer, storing classes_
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fit_transform (y) | Fit the label sets binarizer and transform the given label sets |
get_params ([deep]) | Get parameters for this estimator. |
inverse_transform (yt) | Transform the given indicator matrix into label sets |
set_params (**params) | Set the parameters of this estimator. |
transform (y) | Transform the given label sets |
__init__(classes=None, sparse_output=False)
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fit(y)
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Fit the label sets binarizer, storing classes_
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fit_transform(y)
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Fit the label sets binarizer and transform the given label sets
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get_params(deep=True)
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Get parameters for this estimator.
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inverse_transform(yt)
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Transform the given indicator matrix into label sets
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set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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transform(y)
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Transform the given label sets
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© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MultiLabelBinarizer.html