  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.
