A decision tree is a tree-like model of decisions and their consequences, with classification and regression tree (CART) being the most commonly used. Being simple models, decision trees are considered ’weak learners’ relative to more complex and more accurate models. By using a large ensemble of weak learners, methods such as random forest can compete well against strong learners such as neural networks. An alternative to random forest is boosting. While random forest constructs all the trees independently, boosting constructs one tree at a time. At each step, boosting tries to a build a weak learner that improves on the previous one.