Model Ensemble

Model ensemble methods utilize multiple models to make predictions, aiming to enhance the overall performance compared to individual models. This approach capitalizes on the strengths of different algorithms to reduce errors and improve robustness. Common techniques include bagging, boosting, and stacking, which collectively help in achieving better accuracy and generalization.