In order to combat the variability associated with the various feature selection methods, we switched to a consensus model (see Methods for clarification on the creation of the consensus model). This consensus model contains a yet smaller number of features, namely only those that were found to be of high importance in 75 % of all previously used ML methods, while discarding the rest, resulting in 49 features (internal coordinates) (Figure ). For sake of comparison the highly important features of a PCA-only and LDA-only consensus model are depicted in the SI in Figure . It is important to note that individual models assign varying importance scores to each internal coordinate. As a result, the feature importance ranking derived from the overall consensus model is likely to differ from those of the individual models. We performed an additional PCA on the 49 consensus features (Figure ). As evident from Table , in this model PC1 shows the highest variance of all PCAs. Judging the importance of the consensus model features (Figure ), we see that the C-N bond distance is most important (in agreement with chemical intuition), alongside several distances and angles corresponding to the reaction centre. Notably, the c6-C distance is also deemed highly important, which indicates that the cage indeed plays a role in defining the reaction coordinate. This small set of internal coordinates yields almost perfect separation of the three states, as well as distinguishing the product conformations (Figure ), thereby outperforming any of the individual feature selection methods.