An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data

• URL: http://arxiv.org/abs/2106.09111v1
• Date: Wed, 16 Jun 2021 20:30:26 GMT
• Title: An Imprecise SHAP as a Tool for Explaining the Class Probability Distributions under Limited Training Data
• Authors: Lev V. Utkin and Andrei V. Konstantinov and Kirill A. Vishniakov
• Abstract summary: An imprecise SHAP is proposed for cases when the class probability distributions are imprecise and represented by sets of distributions. The first idea behind the imprecise SHAP is a new approach for computing the marginal contribution of a feature. The second idea is an attempt to consider a general approach to calculating and reducing interval-valued Shapley values.
• Score: 5.8010446129208155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the most popular methods of the machine learning prediction explanation is the SHapley Additive exPlanations method (SHAP). An imprecise SHAP as a modification of the original SHAP is proposed for cases when the class probability distributions are imprecise and represented by sets of distributions. The first idea behind the imprecise SHAP is a new approach for computing the marginal contribution of a feature, which fulfils the important efficiency property of Shapley values. The second idea is an attempt to consider a general approach to calculating and reducing interval-valued Shapley values, which is similar to the idea of reachable probability intervals in the imprecise probability theory. A simple special implementation of the general approach in the form of linear optimization problems is proposed, which is based on using the Kolmogorov-Smirnov distance and imprecise contamination models. Numerical examples with synthetic and real data illustrate the imprecise SHAP.

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