Understanding the behavior of a black-box model with probabilistic inputs can be based on the decomposition of a parameter of interest (e.g., its variance) into contributions attributed to each coalition of inputs (i.e., subsets of inputs). In this paper, we produce conditions for obtaining unambiguous and interpretable decompositions of very general parameters of interest. This allows to recover known decompositions, holding under weaker assumptions than stated in the literature.
翻译:理解带有概率投入的黑盒模型的行为,可以基于将一个利益参数(例如其差异)分解为每个投入联盟(即投入子集)的贡献。 在本文中,我们为获得对非常一般的利益参数的清晰和可解释的分解创造了条件。 这样可以恢复已知的分解,在比文献中描述的更弱的假设下保持。