class sklearn.preprocessing.LabelEncoder
[source]
Encode labels with value between 0 and n_classes-1.
Read more in the User Guide.
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See also
sklearn.preprocessing.OrdinalEncoder
LabelEncoder
can be used to normalize labels.
>>> from sklearn import preprocessing >>> le = preprocessing.LabelEncoder() >>> le.fit([1, 2, 2, 6]) LabelEncoder() >>> le.classes_ array([1, 2, 6]) >>> le.transform([1, 1, 2, 6]) array([0, 0, 1, 2]...) >>> le.inverse_transform([0, 0, 1, 2]) array([1, 1, 2, 6])
It can also be used to transform non-numerical labels (as long as they are hashable and comparable) to numerical labels.
>>> le = preprocessing.LabelEncoder() >>> le.fit(["paris", "paris", "tokyo", "amsterdam"]) LabelEncoder() >>> list(le.classes_) ['amsterdam', 'paris', 'tokyo'] >>> le.transform(["tokyo", "tokyo", "paris"]) array([2, 2, 1]...) >>> list(le.inverse_transform([2, 2, 1])) ['tokyo', 'tokyo', 'paris']
fit (y) | Fit label encoder |
fit_transform (y) | Fit label encoder and return encoded labels |
get_params ([deep]) | Get parameters for this estimator. |
inverse_transform (y) | Transform labels back to original encoding. |
set_params (**params) | Set the parameters of this estimator. |
transform (y) | Transform labels to normalized encoding. |
__init__($self, /, *args, **kwargs)
Initialize self. See help(type(self)) for accurate signature.
fit(y)
[source]
Fit label encoder
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fit_transform(y)
[source]
Fit label encoder and return encoded labels
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get_params(deep=True)
[source]
Get parameters for this estimator.
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inverse_transform(y)
[source]
Transform labels back to original encoding.
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set_params(**params)
[source]
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)
[source]
Transform labels to normalized encoding.
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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.LabelEncoder.html