sklearn.preprocessing.LabelEncoder¶
-
class
sklearn.preprocessing.LabelEncoder[source]¶ Encode target labels with value between 0 and n_classes-1.
This transformer should be used to encode target values, i.e.
y, and not the inputX.Read more in the User Guide.
New in version 0.12.
- Attributes
- classes_array of shape (n_class,)
Holds the label for each class.
See also
sklearn.preprocessing.OrdinalEncoderEncode categorical features using an ordinal encoding scheme.
sklearn.preprocessing.OneHotEncoderEncode categorical features as a one-hot numeric array.
Examples
LabelEncodercan 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']
Methods
fit(y)Fit label encoder
Fit label encoder and return encoded labels
get_params([deep])Get parameters for this estimator.
Transform labels back to original encoding.
set_params(**params)Set the parameters of this estimator.
transform(y)Transform labels to normalized encoding.
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__init__(*args, **kwargs)¶ Initialize self. See help(type(self)) for accurate signature.
-
fit(y)[source]¶ Fit label encoder
- Parameters
- yarray-like of shape (n_samples,)
Target values.
- Returns
- selfreturns an instance of self.
-
fit_transform(y)[source]¶ Fit label encoder and return encoded labels
- Parameters
- yarray-like of shape [n_samples]
Target values.
- Returns
- yarray-like of shape [n_samples]
-
get_params(deep=True)[source]¶ Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsmapping of string to any
Parameter names mapped to their values.
-
inverse_transform(y)[source]¶ Transform labels back to original encoding.
- Parameters
- ynumpy array of shape [n_samples]
Target values.
- Returns
- ynumpy array of shape [n_samples]
-
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.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfobject
Estimator instance.