Summary
In this chapter, we covered two model interpretation methods: feature importance and decision boundaries. We also learned about model interpretation method types and scopes and the three elements that impact interpretability in machine learning. We will keep mentioning these fundamental concepts in subsequent chapters. For a machine learning practitioner, it is paramount to be able to spot them so that we can know what tools to leverage to overcome interpretation challenges. In the next chapter, we will dive deeper into this topic.
