Throughout this work, we refer to the predictions made by and errors resulting from ensembles of submodels. To explain the meaning of different ensemble metrics, we will use ŷi,j (X n ) to denote the model prediction on the input X n (test data molecule n), where i indicates model initialization and j indicates the split configuration of the full data into training, validation and test sets. The target for each datapoint is given by y(X n ). For ensembling, we obtain N ens models with different i on the exact same data splits j = 1. The prediction of the ensemble, y(X n ), is given by