What if a model could ask a series of simple yes/no questions about your data and make surprisingly powerful predictions? That’s the core idea behind decision trees, and when you combine many trees into an ensemble, you get some of the most effective tools in machine learning. In this chapter, we’ll see how trees are built, how to stop them from overfitting, and how methods like bagging and boosting turn a collection of weak models into a strong one.