- SHAP - SHapley Additive exPlanations
- A commonly used algorithm to understand what decisions ML models are making
- Based on Shapley values, a concept from game theory
- The "game" is reproducing the outcome of the model
- The "players" are the features included in the model
- ==Shapley quantifies the contribution that each player brings to the game==
- ==SHAP quantifies the contribution that each feature briings to the prediction made by the model==
- It calculates the marginal contribution between each variation of the model with increasing number of features
- The marginal contributions are weighted by the reciprocal of the number of features in that category
- summing the SHAP values of each feature of a given observation yields the difference between the prediction of the model and the null model
- Common libraries employ approximations and sampling to avoid training 2F models for F features