Feature ranking of pointwise descriptor vectors occurs in the finest possible features for the data set (on the level of individual 'pixels' of absorption). However, spectral trends of interest to us, such as the presence or absence of a small peak, or the local curvature at a given energy range, are not discernible at that level of feature ranking. While these trends are possibly captured in the structure of decision trees, it is not obvious how to extract them from feature rankings which only highlight the importance of individual values of an input µ(E) vector. In contrast, multiscale polynomials concisely describe local trends across varying energy ranges of the spectrum. Many chemical features such as hybridization and forbidden transitions have information embedded in the shape of the spectrum. Furthermore, work in the saliency map literature found that identically performing models with more easily understandable function were rated by untrained users as more trustworthy ; this may justify the appeal of polynomial featurization even when performance is comparable to pointwise featurization.