Descriptors often do not contribute equally to a machine learning model and by interpreting a trained model we can identify the crucial features. One advantage of the regression trees used by the GBR model is that it intrinsically executes feature selection, performed during construction of the trees. This information can be readily interpreted to find the relative influence of each of the chosen descriptors. This information is plotted in Figure , showing the weight of the top contributing descriptors in the GBR model. We find that local descriptors significantly contribute to the model for both K and G: parameters related to the Si-O bonds and Si-O-Si angles have the largest weight in the model. This can be confirmed by direct correlation plots between specific descriptors and mechanical moduli. In particular, as illustrated in Figure , the harmonic mean of the Si-O-Si angles is strongly correlated with K. Frameworks which contain mostly linear Si-O-Si angles, such as MTN, result in very stiff materials with large K. Furthermore, for prediction of G it is observed that a porosity descriptor, largest included free sphere, plays a crucial role. Structures containing large cages such as RWY, which has a cage greater than 15 Å in diameter, exhibit extremely low G.