I'm learning about using the BaggingRegressor()
from scikit learn (https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.BaggingRegressor.html)
Its n_estimators
parameter is defined as:
n_estimators: int, default=10
The number of base estimators in the ensemble.
Am I right in thinking this changes the number of tree-based models running in total?
For example, I am using xgboost, If I set
BaggingRegressor(n_estimators=100)
is it changing the n_estimators
in xgb to be:
xgboost.XGBRegressor(n_estimators=100)
or does it doxgboost.XGBRegressor()*100
?
Also what within BaggingRegressor()
decides the number of random subsets to be created if it's not the number of base estimators?