If Your Model Isn't Explainable, Is It Really *Your* Model?

A good place to start is Noga Gershon Barak's introduction to the open-source InterpretML package. Noga walks us through the ins and outs of the package, and focuses on Explainable Boosting Machine (EBM), "a glassbox model intended to have comparable accuracy to machine learning models such as Random Forest and Boosted Trees as well as interpretability capabilities." Not sure yet which approach is right for you? If you're taking your first steps in this area, it's understandable if you'd like to know a bit more about the various explainability methods out there. Vincent Margot's overview of XAI methods is an accessible entryway for learning about Partial Dependence Plot (PDP), Accumulated Local Effects (ALE), and Individual Conditional Expectation (ICE), among others.