数据挖掘 - 在 XGBoost 中，如何更改 eval 函数并保持相同的目标？ - 吾爱随笔录

我想将目标保持为“reg：linear”和eval_metric定制的 RMSE，如下所示：

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

当我跑的时候，训练功能如下->

model = xgb.train(param_list, xgb_train, num_rounds, watchlist, None, customised_rmse, early_stopping_rounds=30)

我得到的输出是这个->

[0] 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.
[1] train-rmse:14.4936  val-rmse:14.5103    train-custom-rmse:0.588831  val-custom-rmse:0.589902

等等...

我的问题是，它正在使用“rmse”或“custom-rase”或两者进行优化？我必须做些什么来删除“rmse”，因为它默认带有“reg：linear”？