Ensemble Methods

Ensemble methods combine multiple models to improve overall accuracy and robustness. This approach takes advantage of diverse predictions from different algorithms and learners By combining multiple models, ensemble methods can reduce overfitting and increase predictive power Some popular ensemble methods include bagging, boosting, and stacking techniques Ensemble methods are particularly effective when dealing with complex, high-dimensional datasets or noisy data