Practical Data Science with R, Second Edition

10 Exploring advanced methods

This chapter covers

• Decision tree–based models
• Generalized additive models
• Support vector machines

In chapter 7, you learned about linear methods for fitting predictive models. These models are the bread-and-butter methods of machine learning; they are easy to fit; they are small, portable, and efficient; they sometimes provide useful advice; and they can work well in a wide variety of situations. However, they also make strong assumptions about the world: namely, that the outcome is linearly related to all the inputs, and all the inputs contribute additively to the outcome. In this chapter, you will learn about methods that relax these assumptions.

Figure 10.1 represents our mental model for what we'll do in this ...