Class OneVsRestClassifier

sklearn/multiclass.py:202–636

One-vs-the-rest (OvR) multiclass strategy. Also known as one-vs-all, this strategy consists in fitting one classifier per class. For each classifier, the class is fitted against all the other classes. In addition to its computational efficiency (only `n_classes` classifiers are need

class OneVsRestClassifier(
    MultiOutputMixin,
    ClassifierMixin,
    MetaEstimatorMixin,
    BaseEstimator,
):
    """One-vs-the-rest (OvR) multiclass strategy.

    Also known as one-vs-all, this strategy consists in fitting one classifier
    per class. For each classifier, the class is fitted against all the other
    classes. In addition to its computational efficiency (only `n_classes`
    classifiers are needed), one advantage of this approach is its
    interpretability. Since each class is represented by one and one classifier
    only, it is possible to gain knowledge about the class by inspecting its
    corresponding classifier. This is the most commonly used strategy for
    multiclass classification and is a fair default choice.

    OneVsRestClassifier can also be used for multilabel classification. To use
    this feature, provide an indicator matrix for the target `y` when calling
    `.fit`. In other words, the target labels should be formatted as a 2D
    binary (0/1) matrix, where [i, j] == 1 indicates the presence of label j
    in sample i. This estimator uses the binary relevance method to perform
    multilabel classification, which involves training one binary classifier
    independently for each label.

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

    Parameters
    ----------
    estimator : estimator object
        A regressor or a classifier that implements :term:`fit`.
        When a classifier is passed, :term:`decision_function` will be used
        in priority and it will fallback to :term:`predict_proba` if it is not
        available.
        When a regressor is passed, :term:`predict` is used.

    n_jobs : int, default=None
        The number of jobs to use for the computation: the `n_classes`
        one-vs-rest problems are computed in parallel.

        ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.
        ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
        for more details.

        .. versionchanged:: 0.20
           `n_jobs` default changed from 1 to None

    verbose : int, default=0
        The verbosity level, if non zero, progress messages are printed.
        Below 50, the output is sent to stderr. Otherwise, the output is sent
        to stdout. The frequency of the messages increases with the verbosity
        level, reporting all iterations at 10. See :class:`joblib.Parallel` for
        more details.

        .. versionadded:: 1.1

    Attributes
    ----------