Given the strong correlation between experimental hydricity and Δ𝐺 • 𝑚𝑖𝑛 discussed in "Computing C-H hydricities," we expect the reaction site to be the most stable carbocation (lowest hydricity). We treat this prediction as a binary classification task, with '1' representing each molecule's lowest hydricity and '0' representing other hydricities. We transform QM computed and ML predicted hydricities into '1's and '0's and evaluate the ML predictions against the QM computed hydricities (ground truth) using the LightGBM regressor. Additionally, we train a LightGBM classifier on the transformed QM computed hydricities and evaluate its performance against the ground truth. Lastly, BDE values from ALFABET are also transformed into '1's and '0's and evaluated against the QM computed hydricities. As shown in Table , All methods outperform the baseline models (zero rate null model and random classifier), whereas the LightGBM regressor (MCC: 0.88) significantly outperforms the LightGBM classifier (MCC: 0.69) and ALFABET (MCC: 0.67) in the binary classification task. Both the LightGBM classifier and ALFABET excel in predicting '0's but are less effective at predicting '1's as indicated by the lower precision (PPV) and recall (TPR) values. The reaction dataset includes reactions suspected of involving hydride or hydride-like transfer steps, where we hypothesize that the reaction site corresponds to the C-H site with the lowest hydricity. We also apply the QM workflow to the reaction dataset to obtain QM computed hydricities, allowing us to compare methods and assess if hydricity is a suitable parameter for evaluating these reactions. Additionally, we compare ALFABET BDEs to determine if our QM workflow and ML models better predict reaction sites or if radical transfer reactions dominate the dataset. The QM workflow (MCC: 0.52), the regressor (MCC: 0.50), and ALFABET (MCC: 0.53) achieve similar performance, whereas the classifier achieves an MCC of 0.27. The classifier's poor performance suggests that converting QM hydricities to binary values ("0"s and "1"s) leads to significant information loss, consistent with previous results . The reaction dataset is based on the review article by Cernak et al. where the mechanistically distinct innate insertion and H-abstractions reactions are grouped, arguing that both follow the same pattern where carbocation/radical stability is 3