Machine learning is revolutionizing nutrition science by enabling systems to learn from data and make intelligent decisions. However, the complexity of these models often leads to challenges in understanding their decision-making processes, necessitating the development of explainability techniques to foster trust and increase model transparency. An under-explored type of explanation is cohort explanation, which provides explanations to groups of instances with similar characteristics. Unlike traditional methods that focus on individual explanations or global model behavior, cohort explainability bridges the gap by providing unique insights at an intermediate granularity. We propose a novel framework for identifying cohorts within a dataset based on local feature importance scores, aiming to generate concise descriptions of the clusters via tags. We evaluate our framework on a food-based inflammation prediction model and demonstrated that the framework can generate reliable explanations that match domain knowledge.

本研究解决了机器学习模型在营养科学领域面临的可解释性问题，尤其是对群体的解释方法尚未得到充分探讨。论文提出了一种新框架，通过局部特征重要性评分来识别数据集中的群体，并生成基于标签的简洁描述。研究表明，该框架能够生成可靠的解释，符合领域知识，提升了模型的透明度与信任度。

基于标签的群体解释模型来理解炎症预测模型