Class LabelBinarizer

sklearn/preprocessing/_label.py:184–462

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. At learning time, this simpl

class LabelBinarizer(TransformerMixin, BaseEstimator, auto_wrap_output_keys=None):
    """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.

    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 :meth:`inverse_transform` method.

    Read more in the :ref:`User Guide <preprocessing_targets>`.

    Parameters
    ----------
    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
        True if the returned array from transform is desired to be in sparse
        CSR format.

    Attributes
    ----------
    classes_ : ndarray of shape (n_classes,)
        Holds the label for each class.

    y_type_ : str
        Represents the type of the target data as evaluated by
        :func:`~sklearn.utils.multiclass.type_of_target`. Possible type are
        'continuous', 'continuous-multioutput', 'binary', 'multiclass',
        'multiclass-multioutput', 'multilabel-indicator', and 'unknown'.

    sparse_input_ : bool
        `True` if the input data to transform is given as a sparse matrix,
         `False` otherwise.

    See Also
    --------
    label_binarize : Function to perform the transform operation of
        LabelBinarizer with fixed classes.
    OneHotEncoder : Encode categorical features using a one-hot aka one-of-K
        scheme.

    Examples
    --------
    >>> from sklearn.preprocessing import LabelBinarizer
    >>> lb = LabelBinarizer()