The results from the downsampled training sets, as depicted in Figure , exhibit trends consistent with those observed for the largest training set. The RMSRR and RMSMRD For RMSE, Native + Aug outperforms RIGR on smaller datasets, but when the training set includes more than 5,000 molecules, both models show comparable performance. The RMSRR results indicate that data augmentation increasingly reduces resonance variance as the dataset size increases. The trends for RMSMRD show similar performance for RIGR and Native + Aug, both significantly outperforming Native. In conclusion, there appears to be no lower limit on dataset size for using RIGR, as no critical information seems to be lost through the feature reduction employed by RIGR, for this dataset. This is further validated by using Shapley value analysis for the two featurizers, discussed in Section 2.4.