def get_params(self, deep=False):
        """Get parameters."""
        params = super(XGBModel, self).get_params(deep=deep)
        if isinstance(self.kwargs, dict):  # if kwargs is a dict, update params accordingly
            params.update(self.kwargs)
        if params['missing'] is np.nan:
            params['missing'] = None  # sklearn doesn't handle nan. see #4725
        if not params.get('eval_metric', True):
            del params['eval_metric']  # don't give as None param to Booster
        return params
Get parameters.