In the case of the hydroformylation reaction, our attempts to classify ligands as active or inactive using a single-node decision tree were unsuccessful. We instead sought to use a logistic regression classification algorithm. Logistic regression converts a continuous variable (percent conversion) to a binary one (1 = active or 0 = inactive) with a user-defined threshold value (20% conversion in this case). This classifier produces the probability of a catalyst being active as the output. While logistic regression is an established ML technique, it is underutilized in organic chemistry. The algorithm identified a bivariate classification using buried volume and total ligand dipole (Fig. , graph 2 and Fig. ). In the plot, dark blue denotes high probability that the ligand is while red represents a low probability. The solid black line reflects 50% probability of a ligand being active while the dashed lines define the 25% and 75% probability boundaries. To validate this model, an array of additional ligands was experimentally evaluated resulting in a 95% prediction accuracy (Fig. , graph 2).