Example Concept: Ensemble methods, such as bagging, boosting, and stacking, enhance model accuracy by leveraging the strengths of multiple models. Bagging, like Random Forest, reduces variance by averaging predictions from multiple decision trees. Boosting, such as AdaBoost or Gradient Boosting, sequentially trains models to correct errors made by previous ones, reducing bias. Stacking combines different model types, using a meta-learner to improve predictions by learning how to best combine the base models' outputs.