This result entirely agrees with the "Ugly Duckling" theorem. Boruta, another critical baseline, provided a relatively good predictive performance on average (MAE = ca. 0.03) but failed to reach the maximum performance of GPT-4. This reveals the limitations of variable selection procedures that solely rely on the given dataset, much like sparse modeling. The worst predictive performance occurred when descriptors were randomly selected, likely due to the omission of crucial variables affecting the refractive index. The performance of GPR improved with the use of Boruta and GPT-4. Since GPR lacks an internal system for variable selection, the model probably failed with the 'All' option due to an excess of explanatory variables. SVR generally delivered poor predictive performance, possibly due to an overabundance of model degrees of freedom relative to the available data for learning. RFR and GBR models, which typically have high predictive performance, failed to match the conditions of GPT-4 combined with linear models, even when combined with Boruta, for the limited data set in this study. Given the nature of these decision treebased algorithms, it is plausible that they struggled to regress flexibly on small datasets.