Recognizing the trade-off between performance and interpretability
We have briefly touched on this topic before, but high performance often requires complexity, and complexity inhibits interpretability. As studied in Chapter 2, Key Concepts of Interpretability, this complexity comes from primarily three sources: non-linearity, non-monotonicity, and interactivity. If the model adds any complexity, it is compounded by the number and nature of features in your dataset, which by itself is a source of complexity.
Special model properties
These special properties can help make a model more interpretable.
The key property: explainability
In Chapter 1, Interpretation, Interpretability, and Explainability; and Why Does It All Matter?, we discussed why being able to look under the hood of the model and intuitively understand how all its moving parts derive its predictions in a consistent manner is, mostly, what separates explainability from interpretability. This property is also...
