In the previous chapter, we introduced Machine Learning, unsupervised methods, and supervised methods. We focused on unsupervised learning and described some algorithms, we also concentrated on classifiers. We took time to study cluster analysis, focusing on centroids-based algorithms, and we also looked at hierarchical clustering.

We used Rattle to process customer data in order to create different clusters of customers, and then, we used Qlik Sense to visualize these different clusters.

The objective of this chapter is to introduce you to supervised learning. As I explained in the previous chapter, in supervised learning, the computer analyzes a set of examples to learn how to predict the output of a new situation.

We'll focus on Decision Tree Learning, or Decision Trees, because they're widely used and the knowledge learned by the tree is easy to translate to rules in any software, such as Qlik Sense. These rules are easy to...