Table presents the MCC as a function of ensemble size, ranging from 1 to 100 models, showing no significant performance improvement as the ensemble grows. This indicates that the current limitations in SOM prediction are more likely due to noisy labels, model uncertainty, or both, rather than approximation uncertainty. Typically, when approximation uncertainty is the primary issue, increasing the training data improves performance . However, our findings indicate that in this case, expanding the training data will offer limited benefit, highlighting the need to focus on improving data quality and reducing model uncertainty. To improve aleatoric and model uncertainty, we must enhance SOM label accuracy and develop novel biochemical insights that inform better descriptors of metabolic processes.