What is ensemble learning and why is it powerful?

Ensemble learning combines multiple models to improve prediction accuracy and stability.

Ensembles reduce model error by combining predictions from multiple weak learners. Techniques include bagging (parallel training), boosting (sequential correction), and stacking (meta-learning). This improves generalization by reducing variance and/or bias depending on method.