Our optimization commenced with an analysis of reactivity trends (Fig. , step 1) by applying our recently reported method to classify active and inactive ligands using a single-node decision tree. Essentially, this algorithm partitions the ligands as a function of a ligand descriptor at a userdefined reactivity threshold. This step allows for a rigorous approach to data curation in subsequent analyses, while also providing a means to de-risk future ligand selection to those that would produce active catalysts. Analysis of the Hayashi-Heck dataset using a threshold of 5% yield resulted in an excellent reactivity classification defined by the phosphorus lone pair occupancy (Fig. , graph 1 and Fig. ). Ligand electronic effects have been previously identified as key factors in Hayashi-Heck reactions. However, it should be noted that the correlations observed in this, and all models discussed below are not necessarily causal, and therefore, mechanistic conclusions cannot be made on their basis alone. Validation of the threshold was accomplished by exploring ligands predicted to be active (Fig. , graph 1).