(self, X, y=None, **fit_params)
return self.steps[-1][-1].fit_predict(Xt, yr, **fit_params)
Applies fit_predict of last step in pipeline after transforms.

        Applies fit_transforms of a pipeline to the data, followed by the
        fit_predict method of the final estimator in the pipeline. Valid
        only if the final estimator implements fit_predict.

        Parameters
        ----------
        X : iterable
            Training data. Must fulfill input requirements of first step of
            the pipeline.

        y : iterable or None, default: ``None``
            Training targets. Must fulfill label requirements for all steps
            of the pipeline.

        **fit_params : dict of string -> object
            Parameters passed to the :meth:`fit` method of each step, where
            each parameter name is prefixed such that parameter ``p`` for step
            ``s`` has key ``s__p``.

        Returns
        -------
        y_pred : array-like
Applies fit_predict of last step in pipeline after transforms.
Applies fit_predict of last step in pipeline after transforms .
def fit_predict(self, X, y=None, **fit_params):
        """Applies fit_predict of last step in pipeline after transforms.

        Applies fit_transforms of a pipeline to the data, followed by the
        fit_predict method of the final estimator in the pipeline. Valid
        only if the final estimator implements fit_predict.

        Parameters
        ----------
        X : iterable
            Training data. Must fulfill input requirements of first step of
            the pipeline.

        y : iterable or None, default: ``None``
            Training targets. Must fulfill label requirements for all steps
            of the pipeline.

        **fit_params : dict of string -> object
            Parameters passed to the :meth:`fit` method of each step, where
            each parameter name is prefixed such that parameter ``p`` for step
            ``s`` has key ``s__p``.

        Returns
        -------
        y_pred : array-like
        """
        Xt, yr, fit_params = self._fit(X, y, **fit_params)
        return self.steps[-1][-1].fit_predict(Xt, yr, **fit_params)
def fit_predict ( self , X , y = None , * * fit_params ) : Xt , yr , fit_params = self . _fit ( X , y , * * fit_params ) return self . steps [ - 1 ] [ - 1 ] . fit_predict ( Xt , yr , * * fit_params )