To explore the chemical space of organic cocrystals, we performed t-Distributed Stochastic Neighbor Embedding (t-SNE) visualization using data extracted from the CSD (see Supplementary Figure and Supplementary Information Section 2). This analysis provides a comprehensive overview of the structural diversity and chemical relationships among organic cocrystals. The t-SNE plot in Fig. maps the distribution of CA-based cocrystals within the chemical space, categorized by their polar or nonpolar characteristics. Notably, it reveals that polar CA-based cocrystals 5 predominantly form in a 1:1 stoichiometric ratio. However, within the 51 non-solvated CA-based cocrystals in this ratio, only 7 exhibit polar structures, highlighting a significant, yet largely unexplored, potential for discovering new polar cocrystals in this chemical space. To uncover the principles underlying polar cocrystal formation, we include 51 non-solvated CA-based cocrystal structures as the dataset, featuring 18 distinct descriptors, categorized into three groups: four molecular-level descriptors, six intermolecular-level descriptors, and eight crystal-level descriptors. Using these descriptors as input, we trained XGBoost models on the 51 cocrystals to classify whether they are polar cocrystals. To interpret the underlying XGBoost model predictions, we employed Shapley Additive Explanations (SHAP) analysis , which quantifies the contribution of each feature to classification outcomes. Positive SHAP values indicate features that enhance the likelihood of a polar classification, while negative values correspond to nonpolar outcomes. Detailed methodologies for feature extraction and data analysis are provided in the method and Supplementary Information Sections 3 and 4. The results demontrate that molecular-, intermolecular-, and crystal-level features simultaneously contribute to the classification of cocrystals, highlighting the complex interplay of factors that determine their polar characteristics (Fig. ). Among these, crystal-level descriptors were identified as the most influential in predicting polar cocrystals, followed by molecular-level features. This finding is consistent with chemical intuition, suggesting that overall crystal polarization is governed not only by molecular attributes but also by the collective properties of the periodic structure. A pie chart in Fig. illustrates the relative contributions of each feature category, emphasising the dominant role of crystal-level characteristics in predicting polar cocrystals.