Method transform

sklearn/preprocessing/_data.py:2398–2427

Binarize each element of X. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) The data to binarize, element by element. scipy.sparse matrices should be in CSR format to avoid an un-necessary copy.

(self, X, copy=None)

        return self

    def transform(self, X, copy=None):
        """Binarize each element of X.

        Parameters
        ----------
        X : {array-like, sparse matrix} of shape (n_samples, n_features)
            The data to binarize, element by element.
            scipy.sparse matrices should be in CSR format to avoid an
            un-necessary copy.

        copy : bool
            Copy the input X or not.

        Returns
        -------
        X_tr : {ndarray, sparse matrix} of shape (n_samples, n_features)
            Transformed array.
        """
        copy = copy if copy is not None else self.copy
        # TODO: This should be refactored because binarize also calls
        # check_array
        X = validate_data(
            self,
            X,
            accept_sparse=["csr", "csc"],
            force_writeable=True,
            copy=copy,
            reset=False,
        )
        return binarize(X, threshold=self.threshold, copy=False)

    def __sklearn_tags__(self):
        tags = super().__sklearn_tags__()