In this chapter, we learned all about decision trees and how to apply them to both classification and regression tasks. We talked a bit about data generation, overfitting, and ways to avoid this phenomenon by tweaking pre-pruning and post-pruning settings. We also learned about how to rate the quality of a node split using metrics such as the Gini impurity and information gain. Finally, we applied decision trees to medical data to detect cancerous tissues. We will come back to decision trees towards the end of this book when we will combine multiple trees into what is known as a random forest. But for now, let's move on to a new topic.
In the next chapter, we will introduce another staple of the machine learning world: support vector machines.