On the Complexity of SHAP-Score-Based Explanations: Tractability via Knowledge Compilation and Non-Approximability Results

Scores based on Shapley values are widely used for providing explanations to classification results over machine learning models. A prime example of this is the influential~ Shap-score, a version of the Shapley value that can help explain the result of a learned model on a specific entity by assigning a score to every feature. While in general computing Shapley values is a computationally intractable problem, we prove a strong positive result stating that the Shap-score can be computed in polynomial …

classification compilation complexity computing example feature general knowledge machine machine learning machine learning models polynomial positive prime shap value values