Explaining Model Predictions with SHAP Values

Along with cutting-edge modeling techniques such as XGBoost, the practice of explaining model predictions has undergone substantial development in recent years. So far, we've learned that logistic regression coefficients, or feature importances from random forests, can provide insight into the reasons for model predictions. A more powerful technique for explaining model predictions was described in a 2017 paper, A Unified Approach to Interpreting Model Predictions, by Scott Lundberg and Su-In Lee (https://arxiv.org/abs/1705.07874). This technique is known as SHAP (SHapley Additive exPlanations) as it is based on earlier work by mathematician Lloyd Shapley. Shapely developed an area of game theory to understand how coalitions of players can contribute to the overall outcome of a game. Recent machine learning research into model explanation leveraged this concept to consider how groups or coalitions of features in a predictive model...