What is the difference between num_boost_round and n_estimators
I saw that some xgboost methods take a parameter num_boost_round, like this:
model = xgb.cv(params, dtrain, num_boost_round=500, early_stopping_rounds=100)
Others however take n_estimators like this:
model_xgb = xgb.XGBRegressor(n_estimators=360, max_depth=2, learning_rate=0.1)
As far as I understand, each time boosting is applied a new estimator is created. Is that nor correct?
If that is so, then the numbers num_boost_round and n_estimators should be equal, right?
Answer
Yes they are the same, both referring to the same parameter (see the docs here, or the github issue).
The reason of the different name is because xgb.XGBRegressor is an implementation of the scikit-learn API; and scikit-learn conventionally uses n_estimators to refer to the number of boosting stages (for example the GradientBoostingClassifier)