mars.learn.preprocessing.LabelEncoder#

class mars.learn.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 input X.

Read more in the User Guide.

classes_#

Holds the label for each class.

Type

ndarray of shape (n_classes,)

See also

OrdinalEncoder

Encode categorical features using an ordinal encoding scheme.

OneHotEncoder

Encode 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']
__init__()#

Methods

__init__()

fit(y[, session, run_kwargs, execute])

Fit label encoder.

fit_transform(y[, session, run_kwargs])

Fit label encoder and return encoded labels.

get_params([deep])

Get parameters for this estimator.

inverse_transform(y[, session, run_kwargs])

Transform labels back to original encoding.

set_params(**params)

Set the parameters of this estimator.

transform(y[, session, run_kwargs, execute])

Transform labels to normalized encoding.