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

But do they have any other difference?

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

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)

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 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.