In this chapter, we're going to discuss Binary Decision Trees and ensemble methods. Even though they're probably not the most common methods for classification, they offer a good level of simplicity and can be adopted for many tasks that don't require a high level of complexity. They're also quite useful when it's necessary to show how a decision process works because they are based on a structure that can be easily shown in presentations and described step by step.
Ensemble methods are a powerful alternative to complex algorithms because they try to exploit the statistical concept of a majority vote. Many weak learners can be trained to capture different elements and make their own predictions, which are not globally optimal, but using a sufficient number of elements, it's statistically probable that a majority...
