Heid et al. further investigated the separation of uncertainties in machine learning models for predicting chemical properties. Using a synthetic data set of molecular enthalpy values, they emphasized the role of data set size, molecular representations, and model architecture as key factors contributing to model bias. They also examined the effect of ensembling on model performance and provided guidelines for improving models in different uncertainty contexts. Their study shows that errors caused by noise in the training data can be reduced by increasing the data set size, while errors caused by noise in the test data remain irreducible. Furthermore, they used a noise-free setup to demonstrate how the distribution between bias-and variance-related errors depends on the size of the data set. For small data sets, the total error is dominated by model bias, whereas for larger data sets, it becomes dominated by model variance.