In this article, we propose and investigate ML group explainers in a general game-theoretic setting with the focus on coalitional game values and games based on the conditional and marginal expectation of an ML model. The conditional game takes into account the joint distribution of the predictors, while the marginal game depends on the structure of the model. The objective of the article is to unify the two points of view under predictor dependencies and to reduce the complexity of group explanations. To achieve this, we propose a feature grouping technique that employs an information-theoretic measure of dependence and design appropriate groups explainers. Furthermore, in the context of coalitional game values with a two-step formulation, we introduce a theoretical scheme that generates recursive coalitional game values under a partition tree structure and investigate the properties of the corresponding group explainers.

中文翻译：

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具有联盟结构的基于信息的互组解释器，用于机器学习模型解释

在本文中，我们在一般的博弈论背景下提出并研究了ML群体的解释者，重点是联盟博弈的价值以及基于ML模型的条件和边际期望的博弈。条件博弈考虑了预测变量的联合分布，而边际博弈则取决于模型的结构。本文的目的是在预测变量依赖项下统一这两种观点，并降低小组解释的复杂性。为实现此目的，我们提出了一种特征分组技术，该技术采用了信息理论上的依赖性度量，并设计了适当的分组解释器。此外，在具有两个步骤的联盟博弈价值观的背景下，