Explain ensemble methods and their advantages in machine learning.

Ensemble methods combine multiple models to achieve better performance than individual models through diversity and aggregation.\n\n• Bagging: Random Forest, reduces variance through bootstrap sampling\n• Boosting: XGBoost, AdaBoost, reduces bias through sequential learning\n• Stacking: Meta-learner combines base model predictions\n• Voting: Simple averaging or majority voting for final predictions\n\nExample: Kaggle competition uses ensemble of Random Forest, XGBoost, and Neural Network. Stacking with logistic regression meta-learner combines predictions, achieving better performance than individual models through complementary strengths.