What is the difference between Bagging and Boosting?
This question evaluates your understanding of the two main ensemble strategies and their approaches to improving model performance.
Why Interviewers Ask This
Bagging and Boosting are fundamental ensemble techniques. Interviewers ask this to see if you can distinguish between parallel (Bagging) and sequential (Boosting) approaches. They want to know if you understand how each addresses bias and variance.
How to Answer This Question
Define Bagging (Bootstrap Aggregating) as training models in parallel on different data subsets to reduce variance. Define Boosting as training models sequentially, where each new model focuses on correcting errors of the previous one to reduce bias. Compare their goals: Bagging for stability, Boosting for accuracy.
Key Points to Cover
- Bagging trains models in parallel to reduce variance.
- Boosting trains models sequentially to reduce bias.
- Bagging models are independent; Boosting models are dependent.
- Random Forest is Bagging; AdaBoost is Boosting.
Sample Answer
Bagging and Boosting are ensemble techniques but differ in their approach. Bagging, or Bootstrap Aggregating, trains multiple models in parallel on different bootstrap samples of the data to reduce variance and prevent o…
Common Mistakes to Avoid
- Confusing the order of training (parallel vs sequential).
- Not specifying which reduces variance vs bias.
- Mixing up specific algorithms like Random Forest and AdaBoost.
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