Summary

In this chapter, we learned about simple non-linear models for classification and regression called decision trees. Like the parlor game Twenty Questions, decision trees are composed of sequences of questions that examine a test instance. The branches of a decision tree terminate in leaves that specify the predicted value of the response variable. We discussed how to train decision trees using the ID3 algorithm, which recursively splits the training instances into subsets that reduce our uncertainty about the value of the response variable. We useddecision trees to predict whether or not an image on a web page is a banner advertisement. In the next chapter, we willintroduce methods that model a relationship usingcollections of estimators.