What is model ensembling, and how does it improve prediction performance?

Model ensembling is a technique that combines multiple models to improve overall predictive performance compared to individual models. The idea is that different models may capture different patterns in the data, so aggregating their predictions can lead to more robust and accurate results. Common methods for ensembling include bagging (e.g., Random Forests), boosting (e.g., AdaBoost), and stacking (combining different models). By leveraging the strengths of diverse models, ensembling helps reduce overfitting, enhances generalization on unseen data, and often leads to better overall performance.