Considering heterogeneous reactivity of cyclic and acyclic sulfonylimines, motivated us to develop a machine learning model using the successful and unsuccessful reactions. We trained decision tree 45 models using the Extended Connectivity Fingerprints of carboxylic acid and imine (Figure ). We used bootstrapping of several decision tree models to ensure robustness of our model for predicting prospective experimental outcomes (see Supporting text for bootstrapping of the decision tree models for details). A Cohen Kappa statistic of 0.706 was obtained, suggesting strong inter-model reliability on limited training data (20 reactions). All decisions made by the ML model were highly confident except for the final decision (green box in Figure ). This decision is only supported by a single reaction and that reaction is identified by either p-toluic acid or an amine substitution. Therefore, the model is unable to distinguish between specific features that resulted in a successful reaction. To elucidate chemistry at this step, we tested the reaction between 1c (imine without an amine substitution) and 2b (p-toluic acid) and Scheme 5. Reactions performed to test the ML model noted that the reaction occurred. Conversely, the reaction between 1b (imine with an amine substitution) and 2d (4-hydroxy benzoic acid) did not occur. These results show that the final decision should check for p-toluic acid and not an amine substitution. Finally, we tested 2d with the acyclic imine 4a to see if this rule applied to acyclic amines and noted that the reaction does occur. These reactions are shown in Scheme 5 and show how our ML strategy can be used to better understand and expand the substrate scope of an MCR.