What is the difference between xgb.train and xgb.XGBRegressor (or xgb.XGBClassifier)?

Question

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

But do they have any other difference?

Answer 1

@Maxim, as of xgboost 0.90 (or much before), these differences don't exist anymore in that xgboost.XGBClassifier.fit:

• has callbacks
• allows contiunation with the xgb_model parameter
• and supports the same builtin eval metrics or custom eval functions

What I find is different is evals_result, in that it has to be retrieved separately after fit (clf.evals_result()) and the resulting dict is different because it can't take advantage of the name of the evals in the watchlist ( watchlist = [(d_train, 'train'), (d_valid, 'valid')] ) .