Boosting is a machine learning strategy that came into being shortly after researchers discovered the value of “ensembles.” Ensembles are collections of models which are used as a group to make predictions (and classifications) that are often considerably more accurate than individual models. The models are combined either by averaging predictions or using a voting scheme (for classification). Thus, if we built 101 classification models where the output of each model is a prediction of “YES” or “NO” then the ensemble prediction might follow a majority vote rule: predict YES for any record that obtains at least 51 YES votes, and predict “NO” otherwise. Some ensemble methods use weighted voting where the weights reflect the predictive accuracy of the individual models. In this post we want to focus on a few key ideas related to Salford products rather than the scientific field (we will do that in another post or paper).