What is ensemble learning in classification?
Updated May 15, 2026
Short answer
Ensemble learning combines multiple models to improve classification performance.
Deep explanation
Techniques like bagging, boosting, and stacking reduce variance and bias by aggregating predictions.
Real-world example
Fraud detection systems using multiple weak models combined.
Common mistakes
- Assuming ensembles always improve performance.
Follow-up questions
- What is bagging?