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 input X.
Read more in the User Guide.
New in version 0.12.
Attributes:classes_ndarray of shape (n_classes,)Holds the label for each class.
See also
OrdinalEncoderEncode categorical features using an ordinal encoding scheme.
OneHotEncoderEncode categorical features as a one-hot numeric array.
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]) 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_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. |
Fit label encoder.
Parameters:yarray-like of shape (n_samples,)Target values.
Returns:selfreturns an instance of self.Fitted label encoder.
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,)Encoded labels.
Get parameters for this estimator.
Parameters:deepbool, default=TrueIf True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns:paramsdictParameter names mapped to their values.
inverse_transform(y)[source]¶Transform labels back to original encoding.
Parameters:yndarray of shape (n_samples,)Target values.
Returns:yndarray of shape (n_samples,)Original encoding.
set_params(**params)[source]¶Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Parameters:**paramsdictEstimator parameters.
Returns:selfestimator instanceEstimator instance.
transform(y)[source]¶Transform labels to normalized encoding.
Parameters:yarray-like of shape (n_samples,)Target values.
Returns:yarray-like of shape (n_samples,)Labels as normalized encodings.