Considering the 'held out cells' data splits (Fig. ), seven times more models are shown because of the seven data folds. Neural networks did not outperform classical models in this case. In fact, the lowest error across the board was achieved by linear regression applied to config 'A' -the simplest feature configuration. This benchmark error is equal to 264 mV, which is too large for any practical application, yet all other models performed even worse when averaged across folds. To put this into context, an estimator predicting a constant value of 3.875 V (the average voltage in the dataset) would result in a MAE of 270 mV, only slightly underperforming the benchmark. Increasing the number of model parameters does not yield any benefit in this case. On the contrary, more severe overfitting and extreme errors are observed when using high-capacity FNN models with some of the richer feature configurations. This is similar to the linear regression case when applied to high dimensional data, although linear regression was not regularised by early stopping. The application of dropout does not have any obvious effect in the 'held out' case either, other than slightly limiting the number of extreme errors. This suggests that the smoothness of the learned functions was irrelevant to their performance on the test set, and that the learned functions did not generally capture characteristics of the test sets.