Summary
In this chapter, we have learned about how irrelevant features impact model outcomes and how feature selection provides a toolset to solve this problem. We then explored many different methods in this toolset, from the most basic filter methods to the most advanced ones. Lastly, we broached the subject of feature engineering for interpretability. Feature engineering can make for a more interpretable model that will perform better. We will cover this topic in more detail in Chapter 12, Monotonic Constraints and Model Tuning for Interpretability.
In the next chapter, we will discuss methods for bias mitigation and causal inference.
