The figure shows that as the dataset size is increased, both the bias error and variance error decrease steadily (middle left and middle right panels). The bias error starts at a higher level but decreases more steeply, leading to a changing proportion of error due to variance and nonvariance sources (top right panel). The result is that bias error dominates at low dataset sizes and variance error dominates at high dataset sizes, regardless of the model size. In this demonstration, variance error contributes roughly 5-10% of the total error at low dataset sizes and 60-80% of the total error at high dataset sizes. This indicates in large datasets with low noise, that ensembling has potential to be highly effective because the variance errors that it can reduce are so significant. We also can see that in high bias regimes, such as for low dataset sizes, the variance error to be corrected is present but small, making ensembling a less attractive measure. The dataset used for this demonstration is a relatively simple one, so users should expect that the proportions and dataset size needed to transition between regimes will be differ for other datasets.