Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Imprecise SHAP as a Tool for Explaining the Class Probability
  Distributions under Limited Training Data   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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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: quant-ph
6
The categories of this paper are: astro-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: supr-con
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The categories of this paper are: ao-sci
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The categories of this paper are: solv-int
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The categories of this paper are: nucl-ex
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The categories of this paper are: q-fin
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The categories of this paper are: bayes-an
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The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: q-alg