def partial_fit(self, X, y=None, **fit_kwargs):
        """Fit the underlying estimator.

        If this estimator has not been previously fit, this is identical to
        :meth:`Incremental.fit`. If it has been previously fit,
        ``self.estimator_`` is used as the starting point.

        Parameters
        ----------
        X, y : array-like
        **kwargs
            Additional fit-kwargs for the underlying estimator.

        Returns
        -------
        self : object
        """
        estimator = getattr(self, "estimator_", None)
        if estimator is None:
            estimator = sklearn.base.clone(self.estimator)
        return self._fit_for_estimator(estimator, X, y, **fit_kwargs)
Fit the underlying estimator.

        If this estimator has not been previously fit, this is identical to
        :meth:`Incremental.fit`. If it has been previously fit,
        ``self.estimator_`` is used as the starting point.

        Parameters
        ----------
        X, y : array-like
        **kwargs
            Additional fit-kwargs for the underlying estimator.

        Returns
        -------
        self : object