To compare the performance of different types of BACs, we consider both the training and leave-one-out cross-validation errors for species in the reference set. The leave-oneout cross-validation errors are calculated by iteratively holding out one of the 421 reference species to test the model performance while training the BACs, repeating this procedure for all reference species. The cross-validation error provides a more reliable measure of the effectiveness of BACs compared to the training error, which is calculated from model that trained on all 421 reference species at once. Figure and Figure show the distribution of errors for Petersson-type and Melius-type BACs, respectively, for all levels of theory described in candidates for achieving gold-standard thermochemistry. When it comes to the comparison between Melius-type and Petersson-type BACs, we observe similar levels of performance for most of the LOTs. At first glance, it is not easy to determine which type of BAC generally performs better. However, examining the differences between training and cross-validation errors (Table ) provides additional insights into the generalizability of the two BAC methods. Petersson-type BACs consistently achieve lower training errors, while Melius-type BACs In summary, bond additivity correction is a promising approach for achieving high-quality thermochemistry. Although the difference in errors between the two BAC methods is not substantial, especially for higher levels of theory, the potential for better generalization and slightly fewer parameters give the Melius-type BAC a slight advantage. Given the varying cost and performance across LOTs, a future systematic evaluation and benchmark of more LOTs commonly used in the literature for thermochemistry has the potential to offer deeper insights into choosing the optimal LOT for different types of chemistry.