In this section we will use a data set more representative of a typical ML dataset within chemical machine learning. Duan et al. recently published a data set of vertical ionization potential (IP) calculations for transition metal complexes (TMCs) with a range of different DFT functionals . The RAC-155 features developed by the Kulik group are used to represent the transition metal complexes (TMCs) from which a ML model for each functional is trained and used to obtain consensus predictions. Here, we exemplify the use of the above described evaluation metrics by training some simple feed-forward NNs in PyTorch NNs following Amini et al. and Soleimany et al. . The evidential NN is designed to predict four parameters (γ, ν, α and β) for each data sample defining an evidential distribution from which a mean prediction as well as predictions for both aleatoric and epistemic uncertainty can be obtained (see section S2 for details).