This work represents an advance in the scope of ML applications for XANES and the use of feature ranking for generating XANES insights. Our extension of ML-XANES predictions to mean nearest-neighbor distance and Bader charges help to expand the space of inverse problems solvable via data-driven methods. Our study of the importance of featurization also helps to demonstrate how it can improve performance and assist human intepretability.