A relative importance score can be computed for each feature vector component to a random forest model by considering the features which are associated with the greatest reductions in Gini impurity (for classification) and MSE (for regression). Because the ranking occurs by comparing individual values of the input feature vector, careful choice of feature vectors is necessary to capture physically relevant spectrum-property features. During our 10 re-train cycles of each RF, as expected, there was some variability in the feature ranking values due to the randomness of the fitting procedure. However, trends were generally consistent across the fits. The variability associated with each feature vector for each property, featurization, and normalization can be found in the SI.