  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.
