Bagging and Boosting are both ensemble methods, but they have different purposes and perform better in different situations. There isn’t a single “better” method - it depends on your data and problem. Here’s a clear comparison:
Rule of Thumb
- Use bagging if your base model is high-variance (like deep trees) and you want stability.
- Use boosting if your base model is weak and you want to maximize accuracy, but ensure careful tuning.
Comparison Table
| Feature | Bagging | Boosting |
|---|---|---|
| Model Training | Parallel, independent | Sequential, dependent |
| Focus | Reduce variance | Reduce bias |
| Sensitivity | Less sensitive to noise | Sensitive to noise |
| Typical Use | Random Forest | AdaBoost, XGBoost |
| Best For | High-variance models | Weak learners needing improvement |