`sklearn.preprocessing`.label_binarize¶

sklearn.preprocessing.label_binarize(y, *, classes, neg_label=0, pos_label=1, sparse_output=False)[source]¶

Binarize labels in a one-vs-all fashion.

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

This function makes it possible to compute this transformation for a fixed set of class labels known ahead of time.

Parameters

yarray-like: Sequence of integer labels or multilabel data to encode.
classesarray-like of shape (n_classes,): Uniquely holds the label for each class.
neg_labelint, default=0: Value with which negative labels must be encoded.
pos_labelint, default=1: Value with which positive labels must be encoded.
sparse_outputbool, default=False,: Set to true if output binary array is desired in CSR sparse format.

Returns

Y{ndarray, sparse matrix} of shape (n_samples, n_classes): Shape will be (n_samples, 1) for binary problems. Sparse matrix will be of CSR format.

See also

LabelBinarizer: Class used to wrap the functionality of label_binarize and allow for fitting to classes independently of the transform operation.

Examples

>>> from sklearn.preprocessing import label_binarize
>>> label_binarize([1, 6], classes=[1, 2, 4, 6])
array([[1, 0, 0, 0],
       [0, 0, 0, 1]])

The class ordering is preserved:

>>> label_binarize([1, 6], classes=[1, 6, 4, 2])
array([[1, 0, 0, 0],
       [0, 1, 0, 0]])

Binary targets transform to a column vector

>>> label_binarize(['yes', 'no', 'no', 'yes'], classes=['no', 'yes'])
array([[1],
       [0],
       [0],
       [1]])

Examples using `sklearn.preprocessing.label_binarize`¶

sklearn.preprocessing.label_binarize¶

Examples using sklearn.preprocessing.label_binarize¶

`sklearn.preprocessing`.label_binarize¶

Examples using `sklearn.preprocessing.label_binarize`¶