Explainable AI (XAI) Methods Part 2— Individual Conditional Expectation (ICE) Curves. Tutorial on Individual Conditional Expectation (ICE) Curves, its advantages and disadvantages, how it is different from PDP and how to make….

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.

Machine learning (ML) is powerful. Its models and their interpretability have been the subject of increasing attention over the last few years, as they have grown more powerful and widely used. With the right data, machine learning models can predict new data extremely well with little to no interpretability, but interpretability is important for many reasons. Model interpretability allows us to address some of our most fundamental questions about the predictions that a model makes: What features did you learn? Why did you make this prediction?

AutoML (Automated Machine Learning) platforms are getting more and more popular these days, as they allow us to automate the process of applying machine learning end-to-end. This offers the additional advantages of producing quicker and more straightforward solutions and models that quite often outperform hand-designed models. There are several such paid and open-source AutoML platforms in the market like H2O, Data Robot, Google AutoML, TPOT, Auto-Sklearn, etc. All of them come with their pros and cons, and I don't get into the debate of which one is the best of all. Instead, this article focuses on one of the latest features I observed in H2O AutoML -- "Model Explainability".