Ensemble Method

Ensemble methods involve the combination of multiple individual models to produce a more accurate and robust prediction. By leveraging the strengths of various algorithms, ensembles can reduce the risk of overfitting and enhance predictive performance compared to single models. Common ensemble techniques include bagging, boosting, and stacking, which each employ different strategies for model combination and training.