In the final episode of this mini-series, Shea and Anders cover the other common tree-based ensemble model, the Gradient Boosting Machine. Like Random Forests, GBMs make use of a large number of decision trees, but they use a “boosting” approach that cleverly makes use of “weak learners” to incrementally extract information from the data. After an explanation of how GBMs work, we compare them to Random Forests and go over a few examples where they have used GBMs in their own work.