How does ensemble learning reduce variance?
Updated May 15, 2026
Short answer
Ensemble methods reduce variance by averaging predictions from multiple models.
Deep explanation
Ensemble learning combines predictions from multiple weak or diverse models. Techniques like bagging reduce variance by averaging outputs, while boosting reduces bias. Random Forest is a classic example where multiple decision trees trained on different data subsets produce more stable predictions.
Real-world example
Random Forest improves stability in credit risk scoring compared to a single decision tree.
Common mistakes
- Thinking ensembles always reduce bias.
Follow-up questions
- What is bagging?
- What is boosting?