In order to understand the factors influencing accuracy in more detail we performed a feature importance study summarized in Fig. . The feature importance is the average gain, i. e. the relative contribution of each feature for a given prediction over . all targets. In line with the exploratory analysis described above, we observed that the total number of carbons, bonds, rings, and the difference in carbon content within the reaction does not significantly influence the performance of SynRBL. Surprisingly, the disparity in the bond count after graph merging emerged as the most impact factor, surpassing even the number of bond changes in predictive power. In order to investigate the interplay between the most informative factors, we also considered the co-occurances of the number of different bonds after merging, the number of different rings after merging, the count of boundaries detected, and total number of compounds, see Fig. .