This tutorial paper provides a general overview of symbolic regression (SR) with specific focus on standards of interpretability. We posit that interpretable modeling, although its definition is still disputed in the literature, is a practical way to support the evaluation of successful information fusion. In order to convey the benefits of SR as a modeling technique, we demonstrate an application within the field of health and nutrition using publicly available National Health and Nutrition Examination Survey (NHANES) data from the Centers for Disease Control and Prevention (CDC), fusing together anthropometric markers into a simple mathematical expression to estimate body fat percentage. We discuss the advantages and challenges associated with SR modeling and provide qualitative and quantitative analyses of the learned models.

翻译：本教程对符号回归（SR）进行了总体概述，特别关注其可解释性标准。我们认为，尽管文献中对可解释性建模的定义仍存在争议，但该方法是支持评估成功信息融合的实用途径。为说明SR作为一种建模技术的优势，我们向读者展示了其在健康与营养领域的一项应用：利用美国疾病控制与预防中心（CDC）公开的国家健康与营养调查（NHANES）数据，将人体测量指标融合为一个简单的数学表达式以估算体脂百分比。我们讨论了SR建模相关的优势与挑战，并对学习得到的模型进行了定性与定量分析。