Method predict

sklearn/multiclass.py:1240–1263

Predict multi-class targets using underlying estimators. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Data. Returns ------- y : ndarray of shape (n_samples,) Predicted multi-class targ

(self, X)

        return self

    def predict(self, X):
        """Predict multi-class targets using underlying estimators.

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

        Returns
        -------
        y : ndarray of shape (n_samples,)
            Predicted multi-class targets.
        """
        check_is_fitted(self)
        # ArgKmin only accepts C-contiguous array. The aggregated predictions need to be
        # transposed. We therefore create an F-contiguous array to avoid a copy and have
        # a C-contiguous array after the transpose operation.
        Y = np.array(
            [_predict_binary(e, X) for e in self.estimators_],
            order="F",
            dtype=np.float64,
        ).T
        pred = pairwise_distances_argmin(Y, self.code_book_, metric="euclidean")
        return self.classes_[pred]

    def get_metadata_routing(self):
        """Get metadata routing of this object.