As suggested by Pernot , we also use the Var(Z) ? = 1 and the µ(Z) ? = 0 test to check for average calibration and bias, respectively. The results for the RF models are summarized in Table and analyzed in section S5. In short, these tests also reveal problems with average calibration for the N train = 100 and N train = 500 models and these models are also seen to be biased (a tendency of overshooting the logP value). The remaining models are generally closer to being average calibrated but only the N train = 1000 model as well as the two models with most training data (N train = 100k and N train = 150k) pass the test for being average calibrated. This is contrary to the conclusions drawn based on the miscalibration area, A mis (Table ) highlighting how even evaluation metrics targeting the same property of the uncertainties can lead to different conclusions.