sklearn.preprocessing.LabelEncoder¶
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class
sklearn.preprocessing.LabelEncoder[source]¶ Encode labels with value between 0 and n_classes-1.
Read more in the User Guide.
Attributes: - classes_ : array of shape (n_class,)
Holds the label for each class.
See also
sklearn.preprocessing.OrdinalEncoder- encode categorical features using a one-hot or ordinal encoding scheme.
Examples
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]) #doctest: +ELLIPSIS 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"]) #doctest: +ELLIPSIS array([2, 2, 1]...) >>> list(le.inverse_transform([2, 2, 1])) ['tokyo', 'tokyo', 'paris']
Methods
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.
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fit(y)[source]¶ Fit label encoder
Parameters: - y : array-like of shape (n_samples,)
Target values.
Returns: - self : returns an instance of self.
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fit_transform(y)[source]¶ Fit label encoder and return encoded labels
Parameters: - y : array-like of shape [n_samples]
Target values.
Returns: - y : array-like of shape [n_samples]
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get_params(deep=True)[source]¶ Get parameters for this estimator.
Parameters: - deep : boolean, optional
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: - params : mapping of string to any
Parameter names mapped to their values.
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inverse_transform(y)[source]¶ Transform labels back to original encoding.
Parameters: - y : numpy array of shape [n_samples]
Target values.
Returns: - y : numpy array of shape [n_samples]
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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.Returns: - self