The focus of this chapter is on combining the results from different models in order to produce a single model that will outperform individual models on their own. Bagging is essentially an intuitive procedure for combining multiple models trained on the same data set, by using majority voting for classification models and average value for regression models. We'll present this procedure for the classification case, and later show how this is easily extended to handle regression models.