I would like to keep the objective as "reg:linear" and
eval_metric as customized RMSE as follows:
def customised_rmse(preds, dtrain): N = len(preds) preds = np.array(preds) actual = np.array(dtrain.get_label()) crmse = np.sqrt(np.sum(np.power((preds+1)*1.0/(actual+1) - 1.1, 2))/N) return "custom-rmse", crmse
When I ran, train function as follows ->
model = xgb.train(param_list, xgb_train, num_rounds, watchlist, None, customised_rmse, early_stopping_rounds=30)
Output I am getting is this ->
 train-rmse:15.1904 val-rmse:15.2102 train-custom-rmse:0.607681 val-custom-rmse:0.610993 Multiple eval metrics have been passed: 'val-custom-rmse' will be used for early stopping. Will train until val-custom-rmse hasn't improved in 30 rounds.  train-rmse:14.4936 val-rmse:14.5103 train-custom-rmse:0.588831 val-custom-rmse:0.589902
and so on...
My question is, it is getting optimized using "rmse" or "custom-rase" or both? What I have to do to remove "rmse" as it comes by default with "reg:linear"?