The Shapley Value is a solution concept from cooperative game theory that fairly allocates a total payoff among players according to their average marginal contribution across every possible coalition. In machine learning it is repurposed to attribute a model’s prediction to individual input features, treating each feature as a player contributing to the model’s output. This attribution use underlies explainability methods such as SHAP.