Scott Lundberg, Microsoft Research - Explainable Machine Learning with Shapley Values - #H2OWorld

Overview

In this H2O World talk, Microsoft Research's Scott Lundberg explains SHAP (SHapley Additive exPlanations), a method for attributing a model's prediction to its individual input features using game-theoretic Shapley values. Using a loan-risk example, he shows how feature attributions can reveal an accurate model that has learned something wrong, and extends the technique to model monitoring by decomposing prediction loss over time to catch errors after deployment.