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scikit-learn 1.3.2
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# `sklearn.preprocessing`.label_binarize

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

  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:
    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 bool, 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`

Precision-Recall

Precision-Recall