seniorSupervised Learning
What is ensemble learning in supervised learning?
Updated May 17, 2026
Short answer
Ensemble learning combines multiple models to improve prediction accuracy and robustness.
Deep explanation
Ensemble methods aggregate predictions from multiple models to reduce variance, bias, or both. Techniques include bagging (Random Forest), boosting (XGBoost, AdaBoost), and stacking. Bagging reduces variance, boosting reduces bias, and stacking combines diverse model strengths.
Real-world example
Credit scoring systems combining multiple decision trees for robust predictions.
Common mistakes
- Assuming more models always improve performance without tuning.
Follow-up questions
- What is bagging vs boosting?
- What is stacking?