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 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.

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

Parameters:

y : array-like Sequence of integer labels or multilabel data to encode. classes : array-like of shape [n_classes] Uniquely holds the label for each class. neg_label : int (default: 0) Value with which negative labels must be encoded. pos_label : int (default: 1) Value with which positive labels must be encoded. sparse_output : boolean (default: False), Set to true if output binary array is desired in CSR sparse format

Returns:

Y : numpy array or CSR matrix of shape [n_samples, n_classes] Shape will be [n_samples, 1] for binary problems.

See also

LabelBinarizer

class used to wrap the functionality of label_binarize and allow for fitting to classes independently of the transform operation

Examples

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>>> 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:

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>>> 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

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

Examples using sklearn.preprocessing.label_binarize

Precision-Recall

Receiver Operating Characteristic (ROC)