Figure gives an overview of our workflow. Thousands of computational XANES spectra are used to train random forest models for eight absorbing elements; models specific to each element take a spectrum as input to predict the absorbing atom's local coordination number, mean nearest-neighbor distance, and Bader charge. We demonstrate strong performance for coordination number and regression of the mean nearest-neighbor distances, and for some elements, show that Bader charges can be accurately reproduced. We then compare and contrast the interpretability based on property, featurization, and normalization.