As an alternative to using simple empirical rules, we train statistical models for ground state spin prediction, using combinations of features used in the empirical models as part of the input vectors. First, we construct a feature vector F TM , containing only information about the metal center: the metal atomic number, the metal OS, and the number of d electrons. The second vector F CE contains only geometric features of the coordination environment: coordination number, coordination geometry, and relative metal radius. Third, F TM+CE incorporates both F TM and F CE . For comparison, we include the atomic Spectrum of London and Axilrod-Teller-Muto potential (aSLATM) physics-based representation 55 by using a vector that concatenates one, two, and three-body terms associated with the TM as implemented in the QML package with a modified grid and cutoff consistent with our previous work. Finally, F TM+CE+aSLATM concatenates F TM+CE and aSLATM. We employ random forest models trained on the TM-GSspin dataset using all feature vectors. We also train models on individual metal subsets to assess the impact of each metal separately. An overview of the performance of these models is presented in Figure . square planar or octahedral complexes, consistently displaying a singlet or triplet ground state, respectively. For these individual metal subsets, models using F CE are comparable or even more accurate than those using aSLATM, contrasting with the trends obtained for the corresponding models trained on the entire TM-GSspin dataset. This distinction arises because the relationship between the ground state spin and the coordination environment is well-defined within each metal subset but not on the overall dataset, in which each metal exhibits different preferences. Ultimately, the best performance obtained on the individual subsets is consistently achieved for the models employing either F TM+CE or F TM+CE+aSLATM .