The prediction quality heavily depends on the amount of training data available, as shown in Figure C. Adding training data improves the model up to ∼ 90 % of the available data, which corresponds to 14068 individual barriers. Thus, the amount of training data generated at the BMK/6-31+G(2df,p) level is approximately required to reach this accuracy, but also appears to suffice, as the learning curve flattens towards the end. Training curves are shown in Figure . The accuracy decreases for systems with bigger translations and energy barriers, as shown in Figure D and E: increasing the translation cutoff from 2 Å to 3 Å introduces more high-barrier systems to the test set, which seem to be harder to predict exactly. For the prediction of the propagation pathway of a radical in a complex environment, this might be acceptable though, as reactions with high energy barriers are unlikely to occur under ambient conditions. As mentioned, the use of an ensemble model also brings the advantage of an uncertainty measure: the standard deviation between the models. In Figure F, the absolute ensemble error is plotted against the ensemble standard deviation together with a rolling average. For a low standard deviation (smaller than 1.7 kcal/mol), one can assume a low prediction error (< 3 kcal/mol) quite confidently. On the other hand, higher standard deviations no longer scale reliably with the error.