Therefore, it was crucial to develop a method for identifying the ligands with the highest likelihood to produce high yields for a given set of coupling partners. This involved determining the ligands that corroborate the prediction when a combination of substrates is anticipated to yield above 20%, as well as identifying the ligands that challenge the prediction when combinations are expected to yield below 20%. We hypothesized that the optimal ligand could be found by searching for the best performing ligands for substrate combinations with similar properties (determined by the chemical space). To accomplish this, we executed a search to identify the three top-performing ligands for each of the two nearest neighbors of a given product. For instance, P123, which was predicted to produce yields below 20% with an 85% confidence in the prediction-this level of confidence indicates that one or two ligands could yield results exceeding 20%. By identifying its two nearest neighbors (P111 and P125, highlighted with bold black contours in Fig. ) and the corresponding top-three ligand performers for each of these neighbors (L19, L25, L26, L28, and L29, with L28 present in both neighbors), the tool can effectively narrow down the ligand search space for this Ullmann coupling to just five (these ligands are highlighted with asterisk symbols (*) in Fig. ). Remarkably, this ligand suggestion includes the two ligands that exhibited yields above 20% for product P123 (L26 and L29)-our analysis indicates that in 93% of cases, at least one top-performing ligand for a given product is also a top performer among the nearest neighbors (see details in the SI). This example serves as an illustration of the potential applications of the classification workflow. Simply put, utilizing this approach highlights that all the information embedded into the decision tree is useful -even when the overall prediction is "incorrect" predictions can be harnessed to select the most effective ligands.