Since the true values will be precisely defined, high standard deviations imply high errors for one or more of the members, indicating the model is extrapolating beyond the training data and so reflecting the random variation between committee members. In addition to the uncertainty measure, by averaging over predictions, cNNPs have also been shown to have higher accuracy compared to a single NNP. The caveat of cNNPs is an increased cost in training and predictions. This increase can be minimized through parallelization and by storing ACSF vectors.