This paper deals with an important subject in classification problems addressed by machine learning techniques: the evaluation of the influence of each of the features on the classification of individuals. Specifically, a measure of that influence is introduced using the Shapley value of cooperative games. In addition, an axiomatic characterisation of the proposed measure is provided based on properties of efficiency and balanced contributions. Furthermore, some experiments have been designed in order to validate the appropriate performance of such measure. Finally, the methodology introduced is applied to a sample of COVID-19 patients to study the influence of certain demographic or risk factors on various events of interest related to the evolution of the disease.
翻译：本文件论述机器学习技术处理的分类问题的一个重要主题:评价每个特征对个人分类的影响;具体地说,采用合作游戏的沙普利价值来衡量这种影响;此外,根据效率的特性和均衡的贡献,对拟议措施作了不言而喻的定性;此外,还设计了一些实验,以验证这种措施的适当性能;最后,对一些COVID-19病人进行了抽样,以研究某些人口因素或风险因素对与疾病演变有关的各种事件的影响。
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