What is ensemble learning for regression problems?
Updated May 16, 2026
Short answer
Ensemble regression combines multiple models to improve prediction accuracy for continuous outputs.
Deep explanation
In regression tasks, ensemble methods reduce prediction variance and bias by averaging outputs or learning weighted combinations. Bagging methods like Random Forest Regressor reduce variance, while boosting methods like Gradient Boosting Regressor reduce bias. Stacking can further improve performance by learning optimal regression combinations. These methods are especially effective in noisy continuous prediction tasks.
Real-world example
Used in house price prediction systems combining multiple regression models.
Common mistakes
- Using classification intuition directly without adjusting loss functions.
Follow-up questions
- How does regression differ from classification ensembles?
- Which ensemble works best for regression?