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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
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sklearn.preprocessing.OneHotEncoder - encode categorical integer features using a one-hot aka one-of-K scheme.
Examples
LabelEncodercan be used to normalize labels.12345678910>>>fromsklearnimportpreprocessing>>> 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.
123456789>>> 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_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__() -
x.__init__(...) initializes x; see help(type(x)) for 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 :
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transform(y)[source] -
Transform labels to normalized encoding.
Parameters: y : array-like of shape [n_samples]
Target values.
Returns: y : array-like of shape [n_samples]
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preprocessing.LabelEncoder
2025-01-10 15:47:30
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