xgb.train 和 xgb.XGBRegressor(或 xgb.XGBClassifier)有什么区别? [英] What is the difference between xgb.train and xgb.XGBRegressor (or xgb.XGBClassifier)?

问题描述

我已经知道xgboost.XGBRegressor 是 XGBoost 的 Scikit-Learn Wrapper 接口."

I already know "xgboost.XGBRegressor is a Scikit-Learn Wrapper interface for XGBoost."

但它们还有其他区别吗?

But do they have any other difference?

推荐答案

xgboost.train 是通过梯度提升方法训练模型的低级 API.

xgboost.train is the low-level API to train the model via gradient boosting method.

xgboost.XGBRegressor 和 xgboost.XGBClassifier 是准备DMatrix 并传入相应的目标函数和参数.最后，fit 调用简单地归结为:

xgboost.XGBRegressor and xgboost.XGBClassifier are the wrappers (Scikit-Learn-like wrappers, as they call it) that prepare the DMatrix and pass in the corresponding objective function and parameters. In the end, the fit call simply boils down to:

self._Booster = train(params, dmatrix,
                      self.n_estimators, evals=evals,
                      early_stopping_rounds=early_stopping_rounds,
                      evals_result=evals_result, obj=obj, feval=feval,
                      verbose_eval=verbose)

这意味着可以通过 XGBRegressor 和 XGBClassifier 完成的一切都可以通过底层的 xgboost.train 来完成功能.反过来，显然不是这样，例如，XGBModel API 不支持 xgboost.train 的一些有用参数.显着差异的列表包括:

This means that everything that can be done with XGBRegressor and XGBClassifier is doable via underlying xgboost.train function. The other way around it's obviously not true, for instance, some useful parameters of xgboost.train are not supported in XGBModel API. The list of notable differences includes:

• xgboost.train 允许设置在每次迭代结束时应用的回调.
• xgboost.train 允许通过 xgb_model 参数继续训练.
• xgboost.train 不仅允许 eval 函数的最小化，还允许最大化.

• xgboost.train allows to set the callbacks applied at end of each iteration.
• xgboost.train allows training continuation via xgb_model parameter.
• xgboost.train allows not only minization of the eval function, but maximization as well.

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