A random forest regression model was also trained on the thermodynamic features to evaluate their predictive power in a nonlinear tree-based ensemble model. and feature importances are reported in Table . Despite the overfitting, the model identified V m as the most important parameter by far for both the quinone and hydroquinone datasets. For the quinones, the aromatic eccentricity was the second most important parameter, while for the hydroquinones the molecular flexibility dominated. Due to the tendency of this model to overfit the training set (due to the lack of parameter fine-tuning), and its inherent inability to extrapolate, random forest regression is not recommended as the best model structure for this type of prediction.