Machine Learning with R, the tidyverse, and mlr

Chapter 10. Nonlinear regression with generalized additive models

This chapter covers

• Including polynomial terms in linear regression
• Using splines in regression
• Using generalized additive models (GAMs) for nonlinear regression

In chapter 9, I showed you how linear regression can be used to create very interpretable regression models. One of the strongest assumptions made by linear regression is that there is a linear relationship between each predictor variable and the outcome. This is often not the case, so in this chapter I’ll introduce you to a class of models that allows us to model nonlinear relationships in the data.

We’ll start by discussing how we can include polynomial terms in linear regression to model nonlinear relationships, ...