def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
            early_stopping_rounds=None, verbose=True):
        # pylint: disable = attribute-defined-outside-init,arguments-differ
        """
        Fit gradient boosting classifier

        Parameters
        ----------
        X : array_like
            Feature matrix
        y : array_like
            Labels
        sample_weight : array_like
            Weight for each instance
        eval_set : list, optional
            A list of (X, y) pairs to use as a validation set for
            early-stopping
        eval_metric : str, callable, optional
            If a str, should be a built-in evaluation metric to use. See
            doc/parameter.md. If callable, a custom evaluation metric. The call
            signature is func(y_predicted, y_true) where y_true will be a
            DMatrix object such that you may need to call the get_label
            method. It must return a str, value pair where the str is a name
            for the evaluation and value is the value of the evaluation
            function. This objective is always minimized.
        early_stopping_rounds : int, optional
            Activates early stopping. Validation error needs to decrease at
            least every <early_stopping_rounds> round(s) to continue training.
            Requires at least one item in evals.  If there's more than one,
            will use the last. Returns the model from the last iteration
            (not the best one). If early stopping occurs, the model will
            have two additional fields: bst.best_score and bst.best_iteration.
        verbose : bool
            If `verbose` and an evaluation set is used, writes the evaluation
            metric measured on the validation set to stderr.
        """
        evals_result = {}
        self.classes_ = list(np.unique(y))
        self.n_classes_ = len(self.classes_)
        if self.n_classes_ > 2:
            # Switch to using a multiclass objective in the underlying XGB instance
            self.objective = "multi:softprob"
            xgb_options = self.get_xgb_params()
            xgb_options['num_class'] = self.n_classes_
        else:
            xgb_options = self.get_xgb_params()

        feval = eval_metric if callable(eval_metric) else None
        if eval_metric is not None:
            if callable(eval_metric):
                eval_metric = None
            else:
                xgb_options.update({"eval_metric": eval_metric})

        if eval_set is not None:
            # TODO: use sample_weight if given?
            evals = list(DMatrix(x[0], label=x[1]) for x in eval_set)
            nevals = len(evals)
            eval_names = ["validation_{}".format(i) for i in range(nevals)]
            evals = list(zip(evals, eval_names))
        else:
            evals = ()

        self._le = LabelEncoder().fit(y)
        training_labels = self._le.transform(y)

        if sample_weight is not None:
            train_dmatrix = DMatrix(X, label=training_labels, weight=sample_weight,
                                    missing=self.missing)
        else:
            train_dmatrix = DMatrix(X, label=training_labels,
                                    missing=self.missing)

        self._Booster = train(xgb_options, train_dmatrix, self.n_estimators,
                              evals=evals,
                              early_stopping_rounds=early_stopping_rounds,
                              evals_result=evals_result, feval=feval,
                              verbose_eval=verbose)

        if evals_result:
            for val in evals_result.items():
                evals_result_key = list(val[1].keys())[0]
                evals_result[val[0]][evals_result_key] = val[1][evals_result_key]
            self.evals_result_ = evals_result

        if early_stopping_rounds is not None:
            self.best_score = self._Booster.best_score
            self.best_iteration = self._Booster.best_iteration

        return self