For the quinone dataset, there is no overlap between the 10 most important features between the ridge regression and random forest model. This feature importance discrepancy implies that the model is not capturing any of the underlying physical relationships that determine T m . For the hydroquinone dataset, the only feature that overlaps between the 10 most important features for the ridge regression and random forest models is the molecular weight. Despite the same hydroquinone test set AAE for random forest and ridge regression models (39.5 °C), this feature importance discrepancy indicates a lack of physical relations being captured in the random forest regression. Evidenced by overfitting, it is more likely that the random forest model is not capturing the underlying physical relationships between features, as it does not generalize well from the training set to the test set.