What is ensemble learning and why is it powerful in data mining?

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

Ensemble methods aggregate predictions from multiple weak learners to form a stronger model. Techniques include bagging (Random Forest), boosting (XGBoost, AdaBoost), and stacking. Bagging reduces variance by averaging models trained on different subsets, while boosting reduces bias by sequentially correcting errors. In data mining, ensembles significantly improve predictive performance on noisy, large-scale datasets.