Ensemble methods involve making predictions using combinations of different ML models. For example, a random forest is a collection of decision tree classifiers that have been decorrelated from each other by choosing and using specific feature sets for each tree. Additionally, AdaBoost is an algorithm that fits many weak learners on the data to make effective predictions. These algorithms are supported by the sklearn.ensemble module.