One of the reasons that these 18 complexes lack reported T1/2 values is that they undergo two-step SCO. We expect ML-predicted T1/2 values to be in agreement with estimated T1/2 values only for complexes that show single-step SCO behavior because the models were trained on such complexes. Indeed, in the blind set of 18 complexes, we observe that for some of the complexes that exhibit two-step SCO behavior, ML-predicted T1/2 values are significantly different from the estimated T1/2 values (Figure and Supporting Information Figure and Table ). For example, ML models considerably underestimate the T1/2 of an SCO complex (refcode: IZADUK ) that exhibits two-step SCO behavior, i.e., ML-predicted T1/2 of ca. 190 K vs estimated T1/2 of 300 K (Figure and Supporting Information Table ). However, for a smaller test set of 10 SCO complexes obtained after removing the complexes with two-step SCO behavior, RF-RFA RF and RF models show improved correlation to estimated T1/2 values with a Pearson's r of 0.40 and 0.32, respectively (Supporting Information Table ). We also observe that ML models strongly overestimate the T1/2 of a blind set complex (refcode: DUBFOY 133 ) that contains boron atoms, i.e., ML-predicted T1/2 of ca. 250 K vs estimated T1/2 of 102 K (Figure ). Further examination reveals that all the complexes in the training set that contain boron atoms show large T1/2 values (>180 K) which likely causes the larger ML-predicted T1/2 of DUBFOY. Excluding this complex leads to very strong correlation of RF and RF-RFA RF-predicted T1/2 values to estimated T1/2 values over the resultant 9 SCO complex set, with a Pearson's r of 0.74 and 0.82, and Spearman's r of 0.55 and 0.67, respectively (Supporting Information Table and Figure ). Finally, we compare ML T1/2 predictions on the blind set to DFA T1/2 predictions from the three best-performing functionals, i.e., modB3LYP (aHF = 0.10), TPSSh, and M06-L (Figures and). DFA T1/2 predictions show little correlation with estimated T1/2 values for the set of 18 SCO complexes as quantified by a poor Pearson's r of 0.03-0.08 and very large MAEs of up to 400 K (Figure and Supporting Information Table ). Consistent with prior observations on the test set performance, M06-L performs slightly better than the other two DFAs, with a relatively small MAE of 179 K (Figure and Supporting Information Table ). However, we find that all three DFAs consistently fail at predicting the T1/2 of a complex (refcode: ECODIM 135 ) which shows two-step SCO behavior, i.e., strongly negative DFA-predicted T1/2 values vs estimated T1/2 of 174 K (Figure ). While ML models show improved performance on a smaller set of 9 SCO complexes after removing those with two-step SCO behavior and with boron atoms, the DFA T1/2 predictions show no correlation to estimated T1/2 values even over the smaller set (Supporting Information Tables S5-S6 and Figure ). Overall, RF and RF-RFA RF models outperform the DFA approach in SCO complex transition temperature prediction across the SCO-95 data set, as confirmed through predictions on the set of SCO complexes for which experimental T1/2 values are not reported. Representative structures corresponding to SCO complexes (refcodes: GETSAC, 136 REVCUS, 137 and ECODIM 135 ) are shown as insets, and their data points are highlighted with orange circle, green circle, and red square, respectively. Hydrogen, boron, carbon, nitrogen, sulfur, and iron are shown in white, pink, gray, blue, yellow, and brown, respectively. The parity line is shown in solid gray in the plots.