While Eq. ( ) shows that the absolute error, on average, is related to the committee standard deviation by a factor α = 2/ √ 2πN C , this is only true for a large number of repeated experiments of committee training and predictions. For a single committee prediction ŷ, for example predicting the molecular energy or the force of a single atom, the model error |ŷ| cannot be directly correlated with the uncertainty obtained from the committee standard deviation s, because the prediction corresponds to a single random draw from the underlying distribution. At the same time a large number of repeated committee trainings is prohibitively expensive in practice, so that the correlation of the absolute error and the committee uncertainty through Eq. ( ) might seem inapplicable to machine learning. Alternatively, the conversion factor from Eq. ( ) can also be recovered by averaging over targets within a dataset of N i data points, instead of averaging over multiple experiments of model retraining and prediction for a single target. In this case, the mean absolute error over all targets y i is related to the mean committee standard deviation by a factor 2/ √ 2πN C :