Class OneVsOneClassifier

sklearn/multiclass.py:678–1040

One-vs-one multiclass strategy. This strategy consists in fitting one classifier per class pair. At prediction time, the class which received the most votes is selected. Since it requires to fit `n_classes * (n_classes - 1) / 2` classifiers, this method is usually slower than one-vs

class OneVsOneClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
    """One-vs-one multiclass strategy.

    This strategy consists in fitting one classifier per class pair.
    At prediction time, the class which received the most votes is selected.
    Since it requires to fit `n_classes * (n_classes - 1) / 2` classifiers,
    this method is usually slower than one-vs-the-rest, due to its
    O(n_classes^2) complexity. However, this method may be advantageous for
    algorithms such as kernel algorithms which don't scale well with
    `n_samples`. This is because each individual learning problem only involves
    a small subset of the data whereas, with one-vs-the-rest, the complete
    dataset is used `n_classes` times.

    Read more in the :ref:`User Guide <ovo_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 * (
        n_classes - 1) / 2` OVO 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.

    Attributes
    ----------
    estimators_ : list of ``n_classes * (n_classes - 1) / 2`` estimators
        Estimators used for predictions.

    classes_ : numpy array of shape [n_classes]
        Array containing labels.

    n_classes_ : int
        Number of classes.

    pairwise_indices_ : list, length = ``len(estimators_)``, or ``None``
        Indices of samples used when training the estimators.
        ``None`` when ``estimator``&#x27;s `pairwise` tag is False.

    n_features_in_ : int
        Number of features seen during :term:`fit`.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Defined only when `X`
        has feature names that are all strings.

        .. versionadded:: 1.0