core.py 文件源码

def fit(self, X, y=None):
        """Fit the gradient boosting model

        Parameters
        ----------
        X : array-like [n_samples, n_features]
        y : array-like

        Returns
        -------
        self : the fitted Regressor

        Notes
        -----
        This differs from the XGBoost version not supporting the ``eval_set``,
        ``eval_metric``, ``early_stopping_rounds`` and ``verbose`` fit
        kwargs.
        """
        client = default_client()
        xgb_options = self.get_xgb_params()
        self._Booster = train(client, xgb_options, X, y,
                              num_boost_round=self.n_estimators)
        return self