Interpretation: Interpretable models are desirable because they may help uncover physical insights and relations rather than simply make predictions. Interpretability can also clarify the domain under which the model is valid. Unfortunately, the general situation today is that the most accurate models also tend to be the most opaque. There exist many model-agnostic interpretation methods that can be applied on top of such models, such as partial dependence plots, individual conditional expectation plots, Shapley additive explanations , or surrogate modeling. Nevertheless, these methods do not fully capture nor do they explain the decision-making of the underlying model. As an alternative, some recent work has focused on building interpretation within the model itself or in restricting models to symbolic regression . Unfortunately, without a clear metric for quantifying the interpretability of a model, it remains difficult to measure progress in this area.