Our polynomial featurization framework captures the importance of coarse as well as fine region splits simultaneously, building on previous work by Zheng and Chen et al. that studied the relative information content of different spectra, but only on a per-region basis (pre-edge, edge, and post-edge regions), and for predicting coordination number . Our work is further made distinct by our focus on exploring how alternate featurization can aid model interpretability. Because ML as applied to the analysis of XANES spectra is a budding field, the agreements between our work and theirs is heartening for the reproducibility and transferability of both of our findings.