What is overfitting in ensemble models and how is it controlled?

Overfitting in ensembles occurs when models learn noise instead of patterns, controlled using regularization and sampling techniques.

Although ensembles reduce overfitting compared to single models, they can still overfit, especially boosting models. Overfitting occurs when models learn noise patterns in training data. It can be controlled using techniques like limiting tree depth, early stopping, learning rate shrinkage, subsampling data, and feature selection. Bagging naturally reduces overfitting by averaging, while boosting requires explicit regularization.