The nature of the transition between the dominant error regimes is of interest as well (Fig. top right panel). Though all of the considered model sizes trend towards higher variance contribution at large dataset sizes, the proportions do not track tightly together. Variance becomes a significant error source at an intermediate dataset size for the smaller model sizes than for the larger model sizes. The reason behind this is more due to differences in variance error behavior rather than bias error behavior. If we exclude the behavior of the hidden size 20 model as an outlier, the bias error versus learning curves (middle left) are relatively tightly clustered, with the error varying by roughly a factor of 2 between hidden sizes 50 and 2000. The variance error learning curves (middle right) are much less tightly clustered, with the error varying by roughly a factor of 10 between hidden sizes 50 and 2000 at intermediate dataset sizes. This trend implies a need to consider larger model sizes when used with intermediate dataset sizes in order to avoid the onset of significant variance losses.