Decision trees are intuitive algorithms that are capable of performing classification and regression tasks. They allow users to print out their decision rules, which is a plus when communicating the decisions you made to business personnel and non-technical third parties. Additionally, decision trees are easy to configure since they have a limited number of hyperparameters. The two main decisions you need to make when training a decision tree are your splitting criterion and how to control the growth of your tree to have a good balance between overfitting and underfitting. Your understanding of the limitations of the tree's decision boundaries is paramount in deciding whether the algorithm is good enough for the problem at hand.
In this chapter, we looked at how decision trees learn and used them to classify a well-known dataset. We also learned about the different evaluation metrics and how the size of our data affects our confidence in a model's accuracy...
