Summary

In this chapter, we explored how to evaluate regression models. We learned about using residuals to calculate MAE and RMSE, and how to use these metrics to compare models. We also learned about lasso regression and how it can be used for feature selection. Finally, we learned about tree-based regression models, and looked at how they are able to fit to some of the non-linear relationships that linear regression is unable to handle.

In the next chapter, we will learn about classification models, the other primary type of supervised learning models.