Figure (c) further quantifies the dependence of the expected model error as a function of T , S, and C K . The expected MAE generally increases with Random < Structure < Composition < Chemsys < SG# << Elements, where error bars correspond to σ({MAE} K ). Several key conclusions arise. For this particular dataset and model, using a single training model for inference (blue bars) generally produces an expected MAE ∼10-20% higher than using the ensemble of models from nested CV (green bars) across all C K . From a different perspective, one would overestimate the expected MAEs by ∼10-20% if using the ensemble of non-nested K-fold models to perform inference for materials screening exercises, compared to the MAEs calculated by nested (K, L)-folds.