Linear models are the biggest idea in applied statistics and predictive analytics. There are massive volumes written about even the smallest details of linear regression. As such, there are some important ideas that we can't go over here because of space concerns, or because it requires knowledge beyond the scope of this book. So you don't feel like you're in the dark, though, here are some of the topics we didn't cover—that I would have liked to—and why they are neat:
- Regularization: Regularization was mentioned briefly in the subsection about balancing bias and variance. In this context, regularization is a technique wherein we penalize models for complexity, to varying degrees. My favorite method of regularizing linear models is by using elastic-net regression. It is a fantastic technique and, if you are interested in learning more about it, I suggest you install and read the vignette of the
glmnetpackage, with the help of the following code:
install.packages(...