Hands-On Ensemble Learning with Python

Decision trees

Decision trees are less of a black box than other machine learning algorithms. They can easily explain how they produce a prediction, which is called interpretability. The main concept is that they produce rules by splitting the training set using the provided features. By iteratively splitting the data, a tree form is produced, thus this is where their name derives from. Let's consider a dataset where the instances are individual persons deciding on their vacations.

The dataset features consist of the person's age and available money, while the target is their preferred destination, one of either Summer Camp, Lake, or Bahamas. A possible decision tree model is depicted in the following figure:

Decision tree model for the ...