Ensemble methods are machine learning methods that construct a set of predictive models and combine their outputs into a single prediction. The purpose of combining several models together is to achieve better predictive performance, and it has been shown in a number of cases that ensembles can be more accurate than single models. While some work on ensemble methods has already been done in the 1970s, it was not until the 1990s, and the introduction of methods such as bagging and boosting, that ensemble methods started to be more widely used. Today, they represent a standard machine learning method which has to be considered whenever good predictive accuracy is demanded.