Ensemble methods refer to a type of machine learning technique that combines multiple individual models to produce a stronger predictive model. This approach aims to improve the overall accuracy and generalization ability of machine learning algorithms by leveraging the diversity of multiple models. Ensemble methods typically involve training multiple models on the same dataset using different techniques or algorithms, and then combining their predictions in some way, such as averaging or voting. Popular ensemble methods include random forests, gradient boosting, and bagging. These techniques are widely used in various fields, such as data mining, bioinformatics, and finance, to improve predictive performance and reduce overfitting.