sklearn.preprocessing.label_binarize¶
- sklearn.preprocessing.label_binarize(y, classes, neg_label=0, pos_label=1, sparse_output=False, multilabel=None)¶
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
>>> 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]])
