what is the difference between parameter n_estimator and n_rounds?

[sun137653577]

hi Contributors,
Xgboost is really an exciting tool for data mining. While I am confused with the parameter n_estimator and n_rounds? Per my understanding, both are used as trees numbers or boosting times. But, there is a big difference in predictions. Following are my codes, seek your help. Many thanks.

1. only n_estimators
   clf = XGBRegressor(objective='reg:tweedie',
   learning_rate=0.01,
   n_estimators=500,
   max_depth=6,
   gamma=0.5,
   subsample=1,
   colsample_bytree=0.8,
   reg_alpha=1,
   missing=None)
   xgb_param=clf.get_xgb_params()
   dtrain = xgb.DMatrix(x_train,label=y_train)
   model = xgb.train(xgb_param,dtrain)

2. codes with n_rounds
   clf = XGBRegressor(objective='reg:tweedie',
   learning_rate=0.01,
   max_depth=6,
   gamma=0.5,
   subsample=1,
   colsample_bytree=0.8,
   reg_alpha=1,
   missing=None)
   xgb_param=clf.get_xgb_params()
   n_rounds=500
   dtrain = xgb.DMatrix(x_train,label=y_train)
   model= xgb.train(xgb_param,dtrain,n_rounds)

Jianju

[sethah]

They are the same. You can see it in the source code:

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

In the first instance you aren't passing the num_boost_round parameter and so it defaults to 10. That explains the difference.