These trends in flow rate and dead volume prediction error are likely influenced by the heteroscedasticity of MAE for retention time predictions. In other words, a 30 s absolute error is much more meaningful for a measured RT of 5 minutes compared to one at 25 minutes. Figure plots MAE as a function of RT to better visualize this behaviour. In comparison, Figure plots the mean absolute percent error (MAPE) as a function of RT. Numerically, we observe that the MAE is a smaller fraction (5.1 %, see Figure ) of the average test RT value (𝑅𝑇 𝜇 = 565.0 s), compared to the calculated test MAPE (7.0 %), suggesting that MAE underestimates the relative contribution of smaller RTs. It is clear that MAPE is a more consistent homoscedastic error metric, which we believe provides a more appropriate measure to describe the per-RT prediction error independently of the gradient duration. However, we will report MAE here as well since this is the error metric commonly used in the literature. Finally, to investigate consistency among RP data sets, we plotted the MAE for each individual RP method against the logarithm of the number of instances for that method in the test set (see Figure ). Doing so revealed that the number of RTs for a given method that appear in the test set (and by extension, the entire dataset) does not bias the average performance of the model.