Method partial_fit

sklearn/multioutput.py:412–442

Incrementally fit the model to data, for each output variable. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The input data. y : {array-like, sparse matrix} of shape (n_samples, n_outputs) Multi-output

(self, X, y, sample_weight=None, **partial_fit_params)

    @_available_if_estimator_has("partial_fit")
    def partial_fit(self, X, y, sample_weight=None, **partial_fit_params):
        """Incrementally fit the model to data, for each output variable.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The input data.

        y : {array-like, sparse matrix} of shape (n_samples, n_outputs)
            Multi-output targets.

        sample_weight : array-like of shape (n_samples,), default=None
            Sample weights. If `None`, then samples are equally weighted.
            Only supported if the underlying regressor supports sample
            weights.

        **partial_fit_params : dict of str -> object
            Parameters passed to the ``estimator.partial_fit`` method of each
            sub-estimator.

            Only available if `enable_metadata_routing=True`. See the
            :ref:`User Guide <metadata_routing>`.

            .. versionadded:: 1.3

        Returns
        -------
        self : object
            Returns a fitted instance.
        """
        super().partial_fit(X, y, sample_weight=sample_weight, **partial_fit_params)


class MultiOutputClassifier(ClassifierMixin, _MultiOutputEstimator):