We also anticipate that our work on featurization could come with other benefits. Experimental XAS data often suffers from noise and systematic variation in absorption which can vary even on the same material on the same beamline. Ways to 'coarsen' spectra into features are thus desirable to preserve transferability of model function, and model interpretability, as trends associated with individual regions can be more precisely described compared to pointwise featurization. This interpretability could also be of critical importance in assessing the transferability of a model. If a model with good performance in predicting a given property was found to be depending largely on one feature (say, the presence or lack thereof of a given peak in the spectrum) and that feature was known to be irrelevant (perhaps due to systematic error in a calculation or being associated with the substrate of a material), then it would be unlikely that this model could be reliably transferred to arbitrary experiments that exhibit no such feature.