设置XGBoost提前停止的时间 [英] Setting Tol for XGBoost Early Stopping

问题描述

我正在使用XGBoost并提前停止.在大约1000个时代之后，该模型仍在改进，但是改进的幅度非常低.即:

I am using XGBoost with early stopping. After about 1000 epochs, the model is still improving, but the magnitude of improvement is very low. I.e.:

 clf = xgb.train(params, dtrain, num_boost_round=num_rounds, evals=watchlist, early_stopping_rounds=10)

是否可以为提前停止设置"tol"?即:不触发提前停止所需的最低改进水平.

Is it possible to set a "tol" for early stopping? I.e.: the minimum level of improvement that is required to not trigger early stopping.

Tol是SKLearn模型中的常用参数，例如MLPClassifier和QuadraticDiscriminantAnalysis.谢谢你.

Tol is a common parameter in SKLearn models, such as MLPClassifier and QuadraticDiscriminantAnalysis. Thank you.

推荐答案

我认为xgboost中没有参数tol，但是您可以将early_stopping_round设置得更高.此参数意味着，如果测试集的性能在early_stopping_round时间内没有改善，则它将停止.如果您知道在经过1000个周期之后，您的模型仍在改进，但速度非常缓慢，请将early_stopping_round设置为50，这样对于性能的细微变化将更加宽容".

I do not think that there is a parameter tol in xgboost but you can set the early_stopping_round higher. This parameters means that if the performance on the test set does not improve for early_stopping_round times, then it stops. If you know that after 1000 epochs your model is still improving but very slowly, set early_stopping_round at 50 for example so it will be more "tolerante" about small changes in performance.

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