By using all of the molecules in the dataset to generate one set of fitted parameters, we assume that all molecules have the same types of intermolecular interactions. However, this is unlikely, as the higher melting temperature molecules should have stronger intermolecular interactions (likely hydrogen bonding), and therefore higher enthalpies of melting. By combining different types of molecules in this way, we essentially apply an intermediate strength of intermolecular interaction to all the molecules in the dataset, which results in a "flattening" effect. This "flattening" is also indicative of underfitting in the model, suggesting that there is another important interaction or metric that is not captured by this model.