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DOC Directly import label class in example #26876

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Jul 21, 2023
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DOC Directly import label class in example #26876

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12 changes: 6 additions & 6 deletions sklearn/preprocessing/_label.py
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Original file line number Diff line number Diff line change
Expand Up @@ -56,8 +56,8 @@ class LabelEncoder(TransformerMixin, BaseEstimator):
--------
`LabelEncoder` can be used to normalize labels.

>>> from sklearn import preprocessing
>>> le = preprocessing.LabelEncoder()
>>> from sklearn.preprocessing import LabelEncoder
>>> le = LabelEncoder()
>>> le.fit([1, 2, 2, 6])
LabelEncoder()
>>> le.classes_
Expand All @@ -70,7 +70,7 @@ class LabelEncoder(TransformerMixin, BaseEstimator):
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 = LabelEncoder()
>>> le.fit(["paris", "paris", "tokyo", "amsterdam"])
LabelEncoder()
>>> list(le.classes_)
Expand Down Expand Up @@ -221,8 +221,8 @@ class LabelBinarizer(TransformerMixin, BaseEstimator):

Examples
--------
>>> from sklearn import preprocessing
>>> lb = preprocessing.LabelBinarizer()
>>> from sklearn.preprocessing import LabelBinarizer
>>> lb = LabelBinarizer()
>>> lb.fit([1, 2, 6, 4, 2])
LabelBinarizer()
>>> lb.classes_
Expand All @@ -233,7 +233,7 @@ class LabelBinarizer(TransformerMixin, BaseEstimator):

Binary targets transform to a column vector

>>> lb = preprocessing.LabelBinarizer()
>>> lb = LabelBinarizer()
>>> lb.fit_transform(['yes', 'no', 'no', 'yes'])
array([[1],
[0],
Expand Down