Figure shows trends in bias and variance error when trained on the noiseless group additivity enthalpy dataset. A first noteworthy trend in this dataset is the relationship between variance error and model size (middle right panel). In much the same way that increasing model size improved bias error for all dataset sizes, larger models appear to gradually improve variance performance for all dataset sizes up to a point of diminishing returns. This is significant because it shows a case where adding additional parameters tightens the distribution behavior for the performance of individual model instances. Adding more randomly initialized parameters decreases the randomness of the outcome, presumably due to improved convergence dynamics in a larger model.