What is the difference between bagging and boosting?

Question

Accepted Answer

Bagging and boosting are ensemble learning techniques used to improve model performance.

Bagging involves training multiple models on different subsets of the training data and combining their predictions through averaging or voting.
Boosting involves iteratively training models on the same data, with each subsequent model focusing on the errors of the previous model.
Bagging reduces overfitting and variance, while boosting reduces bias and underfitting.
Examples of bagging algorithms include random forest and bootstrap aggregating. Examples of boosting algorithms include AdaBoost and gradient boosting.