def METHOD_NAME(self, df, evals=(), early_stopping_rounds=None, evals_result=None, verbose_eval=False, **kwargs):
    '''Fit the XGBoost model given a DataFrame.
    This method accepts all key word arguments for the xgboost.train method.
    :param df: A vaex DataFrame containing the features and target on which to train the model.
    :param evals: A list of pairs (DataFrame, string).
        List of items to be evaluated during training, this allows user to watch performance on the validation set.
    :param int early_stopping_rounds: 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).
    :param dict evals_result: A dictionary storing the evaluation results of all the items in *evals*.
    :param bool verbose_eval: Requires at least one item in *evals*.
        If *verbose_eval* is True then the evaluation metric on the validation set is printed at each boosting stage.
    '''
    data = df[self.features].values
    target_data = df[self.target].to_numpy()
    dtrain = xgboost.DMatrix(data, target_data)
    if evals is not None:
        evals = [list(elem) for elem in evals]
        for item in evals:
            data = item[0][self.features].values
            target_data = item[0][self.target].to_numpy()
            item[0] = xgboost.DMatrix(data, target_data)
    else:
        evals = ()
    # This does the actual training / fitting of the xgboost model
    self.booster = xgboost.train(params=self.params,
                                 dtrain=dtrain,
                                 num_boost_round=self.num_boost_round,
                                 evals=evals,
                                 early_stopping_rounds=early_stopping_rounds,
                                 evals_result=evals_result,
                                 verbose_eval=verbose_eval,
                                 **kwargs)