Boosting Machine Learning Models with Explainable AI (XAI)

With a typical machine learning model, the traditional correlation of feature importance analysis often has limited value. In a data scientist's toolkit, are there reliable, systematic, model agnostic methods that measure feature impact accurate to the prediction? As AI gains traction with more applications, Explainable AI (XAI) is an increasingly critical component to explain with clarity and deploy with confidence. XAI technologies are becoming more mature for both machine learning and deep learning. SHAP (SHapley Additive exPlanations) is developed by Scott Lundberg at the University of Washington.