Class OutputCodeClassifier

sklearn/multiclass.py:1043–1289

(Error-Correcting) Output-Code multiclass strategy. Output-code based strategies consist in representing each class with a binary code (an array of 0s and 1s). At fitting time, one binary classifier per bit in the code book is fitted. At prediction time, the classifiers are used to

class OutputCodeClassifier(MetaEstimatorMixin, ClassifierMixin, BaseEstimator):
    """(Error-Correcting) Output-Code multiclass strategy.

    Output-code based strategies consist in representing each class with a
    binary code (an array of 0s and 1s). At fitting time, one binary
    classifier per bit in the code book is fitted.  At prediction time, the
    classifiers are used to project new points in the class space and the class
    closest to the points is chosen. The main advantage of these strategies is
    that the number of classifiers used can be controlled by the user, either
    for compressing the model (0 < `code_size` < 1) or for making the model more
    robust to errors (`code_size` > 1). See the documentation for more details.

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

    Parameters
    ----------
    estimator : estimator object
        An estimator object implementing :term:`fit` and one of
        :term:`decision_function` or :term:`predict_proba`.

    code_size : float, default=1.5
        Percentage of the number of classes to be used to create the code book.
        A number between 0 and 1 will require fewer classifiers than
        one-vs-the-rest. A number greater than 1 will require more classifiers
        than one-vs-the-rest.

    random_state : int, RandomState instance, default=None
        The generator used to initialize the codebook.
        Pass an int for reproducible output across multiple function calls.
        See :term:`Glossary <random_state>`.

    n_jobs : int, default=None
        The number of jobs to use for the computation: the multiclass 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 `int(n_classes * code_size)` estimators
        Estimators used for predictions.

    classes_ : ndarray of shape (n_classes,)
        Array containing labels.

    code_book_ : ndarray of shape (n_classes, `len(estimators_)`)
        Binary array containing the code of each class.

    n_features_in_ : int
        Number of features seen during :term:`fit`. Only defined if the
        underlying estimator exposes such an attribute when fit.

        .. versionadded:: 0.24

    feature_names_in_ : ndarray of shape (`n_features_in_`,)
        Names of features seen during :term:`fit`. Only defined if the