How does gradient boosting differ from bagging?
- A It trains models sequentially, each fitting the errors of the current ensemble
- B It trains models in parallel
- C It uses only one model
- D It requires balanced classes
Answer
It trains models sequentially, each fitting the errors of the current ensemble
Boosting reduces bias where bagging reduces variance, and it is more sensitive to hyperparameters as a result.





