In summary, we trained three ML models to predict transition temperatures of SCO complexes in the previously 40 curated SCO-95 data set. We performed feature selection using RF-RFA to identify the most important features to train transferable models. Of the three ML models we studied, the RF and RF-RFA RF models performed comparably with moderate correlation to experimental T1/2 values. From feature selection, we identified a strong emphasis on global features rather than the more metal-proximal second and third coordination sphere features for predicting T1/2 values. We also found that electronic and geometric features contributed nearly equally to predicting SCO behavior. These observations emphasized exploring metal-distal contributions to molecules to fine-tune Fe(II) SCO design.