The variance error can be reduced by, for example, ensembling, regardless of the other sources of error in the model. There is a trade-off between the gain in model performance and the computational load of training more models. For a quick assessment, we recommend training an ensemble of five models and using the slope of the performance improvement from subsets of the five models to estimate whether additional models should be added. For a more extensive estimation of possible gains from ensembling, we recommend our method for projecting the expected error of different ensemble sizes from Ref. 60. Depending on the task, dataset, and architecture, but also the availability of computation power and intended use of the model, a different number of ensemble models will lead to the best trade-off between performance and computational workload.