As the next step, our goal was to create a ML model characterized by simplicity and robust interpretability resulting in the exploration of decision tree classifiers. These algorithms operate by recursively and procedurally dividing the structural feature space within a training dataset . We trained a decision tree utilizing the complete array of descriptors for substrates and ligands, employing a training/test split ratio of 70:30 (for further details, see SI). Our findings revealed that the most basic decision tree, consisting of only three nodes (one corresponding to each reaction component, as described below) was able to classify combinations of aryl-bromides, primary amines, and ligands into on or off reactivity with high accuracy (Fig. ). Specifically, the model achieved 88% accuracy on the test set, along with an average accuracy of 87% across a 4-fold cross-validation procedure.