In spite of increased attention on explainable machine learning models, explaining multi-output predictions has not yet been extensively addressed. Methods that use Shapley values to attribute feature contributions to the decision making are one of the most popular approaches to explain local individual and global predictions. By considering each output separately in multi-output tasks, these methods fail to provide complete feature explanations. We propose Shapley Chains to overcome this issue by including label interdependencies in the explanation design process. Shapley Chains assign Shapley values as feature importance scores in multi-output classification using classifier chains, by separating the direct and indirect influence of these feature scores. Compared to existing methods, this approach allows to attribute a more complete feature contribution to the predictions of multi-output classification tasks. We provide a mechanism to distribute the hidden contributions of the outputs with respect to a given chaining order of these outputs. Moreover, we show how our approach can reveal indirect feature contributions missed by existing approaches. Shapley Chains help to emphasize the real learning factors in multi-output applications and allows a better understanding of the flow of information through output interdependencies in synthetic and real-world datasets.

翻译：尽管可解释机器学习模型日益受到关注，但多输出预测的解释问题尚未得到广泛解决。基于Shapley值归因特征对决策贡献的方法，是解释局部个体与全局预测最流行的途径之一。在多输出任务中，这些方法单独考虑每个输出，未能提供完整的特征解释。我们提出Shapley链，通过在解释设计过程中纳入标签间依赖关系来解决该问题。Shapley链利用分类器链，通过分离特征得分的直接与间接影响，在多输出分类中为特征重要性评分赋予Shapley值。与现有方法相比，该方案能够将更完整的特征贡献归因于多输出分类任务的预测。我们提出了一种机制，根据输出间的给定链式顺序，分配输出中的隐藏贡献。此外，我们展示了方法如何揭示现有方法遗漏的间接特征贡献。Shapley链有助于强调多输出应用中的真实学习因素，并能更好地理解合成与真实数据集中信息通过输出依赖关系的流动过程。