Summary
After reading this chapter, we covered some traditional methods for interpretability and what their limitations are. We learned about intrinsically interpretable models and how to both use them and interpret them, for both regression and classification. We also studied the performance versus interpretability trade-off and some models that attempt not to compromise in this trade-off. We also discovered many practical interpretation challenges involving the roles of feature selection and engineering, hyperparameters, domain experts, and execution speed.
In the next chapter, we will learn more about different interpretation methods to measure the effect of a feature on a model.
