ensemble-methods

Ensemble methods utilize a group of models to solve a particular problem, often leading to improved predictions compared to any single model. Common ensemble techniques include bagging, boosting, and stacking. By leveraging the strengths of different models, ensemble methods can reduce the risk of overfitting and increase prediction accuracy, making them widely used in machine learning competitions and applications.