Ultimately, the choice of method should be guided by the specific requirements and constraints of the study. Delta learning is ideal for achieving high accuracy with minimal high-level data. However, in situations where computational efficiency is a priority or where transition state searches are not feasible during model application, transfer learning and feature engineering may offer more practical alternatives. This work contributes valuable insights into the application of machine learning for chemical reaction engineering, providing practical guidelines for selecting appropriate data augmentation strategies to achieve reliable and accurate predictions in complex reaction systems.