support vector regression (SVR) and adaptive boosting (AdaBoost) to experimentally test their ability to fit the chemical reaction data. Comparison of the different models suggests that RF delivered the best overall performance (using Morgan 2 fingerprints as input), achieving the lowest average RMSE of 22.7% ± 18.8% (standard deviation). On the other hand, GP seems to deliver the worst performance for outof-sample predictions across most of the input features. Feature set 2, consisting of exclusively Morgan 2 fingerprints, delivered the best prediction performance across all models. Additionally, even though the hybrid features (feature sets 11-14) include relevant principal components of the fingerprints and additional steric/electronic information from DFT calculations, they did not outperform the feature set 2. Interestingly, the performance of many of the feature sets was similar to the performance of OHE, which serves as a control (no chemical information), as can be seen in Figure in which all of the error bars of feature sets 1-14 overlap. Thus, it can be concluded that the additional time and expertise required to generate high-fidelity DFT descriptors, based on mechanistic understanding, is unjustified when the improvement in performance of fingerprints alone is only modest.