Furthermore, we expanded our dataset by incorporating 11 newly discovered non-solvated structures, increasing the total number of non-solvated CA-based hydrogen-bonded cocrystals to 62. This expanded dataset enabled a re-evaluation of the contributions of various features to the prediction of polar cocrystals (Supplementary Fig. ). While minor shifts in feature rankings were observed, crystal-level features remain the most influential predictors. Among these, the crystal-level feature mean_NDV retained its position as the most significant, reaffirming its critical role in capturing structural aspects of the crystal that correlate strongly with polarization. Two other key crystal-level features, mean_CN and max_PE, now rank third and fourth, respectively, further underscoing the importance of structural attributes in polar cocrystal formation. Molecular-level features maintained a consistent overall contribution. Notably, the feature B_HOMO_LUMO, which reflects the electronic properties of the molecules, has risen in prominence, now ranking as the second-most significant predictor. This highlights the critical role of molecular electronic characteristics in determining the polar behavior of cocrystals. Based on the XGBoost model, a new Eg threshold of 4.22 eV was identified: molecules with Eg values above this threshold tend to form nonpolar cocrystals, while those with Eg values below the threshold show a higher likelihood of forming polar cocrystals with CA. Among the intermolecular-level features, E_DFTs, which is associated with exchange-repulsion and dispersion interactions, has emerged as the fifth-most important feature, with a slight increase in its contribution. This suggests that subtle intermolecular interactions also influence the formation of polar structures. While other features continue to contribute to the predictions, their impact is comparatively minor relative to the top-ranked crystal-, molecular-, and intermolecular-level features. The expansion of the dataset has provided deeper insights into the structural and electronic determinants of polar cocrystal formation. These insights enable the refinement of predictive thresholds and criteria for molecular screening, improving the likelihood of identifying candidates with high potential for forming polar cocrystals. This iterative approach enhances the robustness of the ML model and facilitates the discovery of functional materials with tailored properties.