Lightgbm early stopping not working propperly

问题

I'm using light gbm for some machine learning task.

I want to use early stopping to find the optimal number of trees given a number of hyperparameters. However, lgbm stops growing trees while still improving on my evaluation metric.

Below I've attached my specifications:

params = {
'max_bin' : [128],
'num_leaves': [8],
'reg_alpha' : [1.2],
'reg_lambda' : [1.2],
'min_data_in_leaf' : [50],
'bagging_fraction' : [0.5],
'learning_rate' : [0.001]
}

mdl = lgb.LGBMClassifier(n_jobs=-1, n_estimators=7000, 
                     **params)
mdl.fit(X_train, y_train, eval_metric='auc', 
          eval_set=[(X_test, y_test)], early_stopping_rounds=2000, 
        categorical_feature=categorical_features, verbose=5)

After some time lightgbm gives me the following result:

lgbm concludes that an auc on 0.7326 is not better than 0.70995 and stops. What am I doing wrong?

回答1:

It is working properly : as said in doc for early stopping :

will stop training if one metric of one validation data doesn’t improve in last early_stopping_round rounds

and your logloss was better at round 1034.

Try to use first_metric_only = True or remove logloss from the list (using metric param)

来源：https://stackoverflow.com/questions/56802708/lightgbm-early-stopping-not-working-propperly