Keras: LabelEncoder
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sklearn.preprocessing.LabelEncoder
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])
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(self, y) Fit label encoder fit_transform(self, y) Fit label encoder and return encoded labels get_params(self[, deep]) Get parameters for this estimator. inverse_transform(self, y) Transform labels back to original encoding. set_params(self, \*\*params) Set the parameters of this estimator. transform(self, y) Transform labels to normalized encoding.