8.23.4. sklearn.preprocessing.LabelBinarizer

class sklearn.preprocessing.LabelBinarizer

Binarize labels in a one-vs-all fashion

Several regression and binary classification algorithms are available in the scikit. A simple way to extend these algorithms to the multi-class classification case is to use the so-called one-vs-all scheme.

At learning time, this simply consists in learning one regressor or binary classifier per class. In doing so, one needs to convert multi-class labels to binary labels (belong or does not belong to the class). LabelBinarizer makes this process easy with the transform method.

At prediction time, one assigns the class for which the corresponding model gave the greatest confidence. LabelBinarizer makes this easy with the inverse_transform method.

Examples

>>> from sklearn import preprocessing
>>> clf = preprocessing.LabelBinarizer()
>>> clf.fit([1, 2, 6, 4, 2])
LabelBinarizer()
>>> clf.classes_
array([1, 2, 4, 6])
>>> clf.transform([1, 6])
array([[ 1.,  0.,  0.,  0.],
       [ 0.,  0.,  0.,  1.]])

>>> clf.fit_transform([(1, 2), (3,)])
array([[ 1.,  1.,  0.],
       [ 0.,  0.,  1.]])
>>> clf.classes_
array([1, 2, 3])

Attributes

classes_: array of shape [n_class]

Holds the label for each class.

Methods

fit(y)	Fit label binarizer
fit_transform(X[, y])	Fit to data, then transform it
inverse_transform(Y[, threshold])	Transform binary labels back to multi-class labels
set_params(**params)	Set the parameters of the estimator.
transform(y)	Transform multi-class labels to binary labels

__init__()

x.__init__(...) initializes x; see help(type(x)) for signature

fit(y)

Fit label binarizer

Parameters :

y : numpy array of shape [n_samples] or sequence of sequences

Target values. In the multilabel case the nested sequences can have variable lengths.

Returns :

self : returns an instance of self.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters :

X : numpy array of shape [n_samples, n_features]

Training set.

y : numpy array of shape [n_samples]

Target values.

Returns :

X_new : numpy array of shape [n_samples, n_features_new]

Transformed array.

Notes

This method just calls fit and transform consecutively, i.e., it is not an optimized implementation of fit_transform, unlike other transformers such as PCA.

inverse_transform(Y, threshold=0)

Transform binary labels back to multi-class labels

Parameters :

Y : numpy array of shape [n_samples, n_classes]

Target values.

threshold : float

Threshold used in the binary and multi-label cases. Use 0.5 when Y contains probabilities.

Returns :

y : numpy array of shape [n_samples] or sequence of sequences

Target values. In the multilabel case the nested sequences can have variable lengths.

Notes

In the case when the binary labels are fractional (probabilistic), inverse_transform chooses the class with the greatest value. Typically, this allows to use the output of a linear model’s decision_function method directly as the input of inverse_transform.

set_params(**params)

Set the parameters of the estimator.

The method works on simple estimators as well as on nested objects (such as pipelines). The former have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns :self :

transform(y)

Transform multi-class labels to binary labels

The output of transform is sometimes referred to by some authors as the 1-of-K coding scheme.

Parameters :

y : numpy array of shape [n_samples] or sequence of sequences

Target values. In the multilabel case the nested sequences can have variable lengths.

Returns :

Y : numpy array of shape [n_samples, n_classes]