Motivated by our observation of good ML model performance in classifying ligands capable of demonstrating hemilability, we further analyzed what features our models emphasize in making this classification. We carried out a feature importance analysis of the final XGBoost model by examining the total gain function of each feature, where we only considered features that contributed at least 1% to the total gain. Consistent with our earlier analysis, we find that the metallocal features (i.e., 1st or 2nd coordination sphere) contribute less (i.e., 25-50%) than the more distal features (i.e., 3rd coordination sphere and global) that contribute ~50-75% to the total prediction for bidentate and tridentate ligand types (Figure ). Conversely, a somewhat larger (~60%) contribution of metal-local features is observed in the tetradentate model (Figure ). We observe similar feature importances, with metal-distal features contributing ~50-75% of the total prediction using an alternative feature importance measure based on Gini impurity in a random forest classifier, and we notably observe reduced contributions from the metal-local features for tetradentates (~50%, Supporting Information Figure ). The significant contribution of metaldistal and global features explains the difficulty associated with predicting ligand hemilability based on previously emphasized heuristics including donor ability . Furthermore, hemilability is a highly balanced property that depends both on structural and electronic features, unlike other properties such as spin state that depend much more strongly on electronic features (Figure ). Finally, to demonstrate the promise of our ML models for ligand discovery in catalyst design, we use the best-performing XGBoost models to make hemilability predictions for all unlabeled ligands in our original dataset. Our model assigns many ligands as candidates for hemilability. For bi-and tetradentate ligands, we obtain bimodal distributions with a similar number of ligands labeled as labile and non-hemilabile, whereas the majority of the tridentate ligands are labeled as hemilabile (Figure ). The prediction of most tridentate ligands to be hemilabile appears likely to be an overprediction of hemilability. For example, a tridentate ligand featuring a cyclic, rigid 1,3,5-triazinane based scaffold (refcode: AROGAQ), is confidently (probability = 0.94) predicted to be hemilabile, even though partial dissociation would likely cause an increase in the ring strain due to conformation change from chair to twist-boat (Figure ). These trends by ligand denticity are consistent with the performance of the classifier trained on the random set, where bidentate and tetradentate ligands showed better performance on the randomly sampled set, but the tridentate classifier had poorer performance. This suggests that negative examples were particularly difficult to assign accurately for the tridentate model and some degree of label contamination occurred even with semi-supervised learning in selecting negative samples, indicating that the model has not learned well to distinguish positive and negative examples. As a result, when applied to a new space of candidate ligands, the model is likely erroneously overpredicting hemilability on this new set.