Explain the difference between bagging and boosting.

Explain the difference between bagging and boosting.

Approach: Compare parallel vs sequential model building.

Bagging (Bootstrap Aggregating) trains models in parallel on different data subsets and averages them to reduce variance. Boosting trains models sequentially, where each model focuses on the errors of the previous one, primarily to reduce bias.

Follow-up: Which one is Random Forest an example of?

Key concepts: variance reduction, bias reduction.