Shapley Values and Cooperative Game Theory | Machine Learning Interpretability

Shapley values have been used to explain both interpretable and black-box models alike. They've gained popularity partly because they are based on sound mathematical theorems and partly because of the open-source Python library SHAP, which made its use easily available worldwide. Shapley values come from the cooperative game theory field. So in this video, we provide an intuition of how Shapley values are calculated to distribute rewards fairly among players, which in machine learning translates to distributing importance fairly among features. Using a football match example, we illustrate how each player's contribution impacts the outcome and how this concept applies to feature importance in machine learning models. Want to learn more? Check out our course: https://www.trainindata.com/p/machine...