Crucially, similarities and differences in MatFold trends can be observed between different modeling approaches and data domains, as demonstrated in our two exemplars, to draw deeper conclusions about their respective strengths and weaknesses. As expected in both exemplars, purely random splits provide the most biased underestimation of expected MAE, but the evolution of expected MAE with increasingly strict splitting criteria is heavily dependent on the modeling approach and data domain. For GNN's predictions of ∆H V (a direct crystal structure input model), the expected MAE on structures with unseen elements is nearly double that of structures with unseen space groups. Yet the opposite is true for RF predictions of ϕ (a hand-engineered feature input model). Therefore these ∆H V GNN models generalize better to unseen structural motifs than unseen chemistry, the exact opposite of ϕ RF models.