We perform feature selection using RF-RFA with RF and KRR models and observe a better performance of the RF-RFA RF model than the KRR model (see Sec 3b). We identify the 14 most important RAC features using an RF-RFA RF model by training it on T1/2 values of 74 SCO complexes obtained after removing two outliers (refcodes: ESOSOW 124 and EXARAY 123 ) from the set of 76 SCO complexes with experimentally reported T1/2 values (Figure and Supporting Information Table ). As in prior work , we group features as metal-local and metal-distal, where metal-local features correspond to those within two bond paths of the metal, i.e., the metal, its first and second coordination spheres, while metal-distal features correspond to the third coordination sphere and the more distant global features (Figure ). Analysis of model feature importance reveals that all selected features are metal-distal in nature and identifies global features to be more important than the more metal-proximal second and third coordination sphere features for predicting T1/2 values, despite an expectation that the variation of the second and third coordination sphere could drive significant changes in spin state energetics (Figure ). We also find that electronic features, i.e., nuclear charge (Z) and Pauling electronegativity (c), play a slightly more important role (i.e., over 55% of selected features are electronic in nature) than geometric features, i.e., topology (T), covalent radius (S), and identity (I), although the prediction of this property has a greater contribution of geometric effects than observed for the prediction of DFT-calculated spin states of a larger set of molecules 94 (Figure ). To ensure robustness in our observations to the method of feature selection, we compare to RF-RFA KRR models trained to obtain the most important features in the prediction of T1/2 values where 25 features are identified (Figure and Supporting Information Table ). Analysis of the selected RF-RFA KRR features reveals that ca.