Assessing the prediction performance on unseen data as in this challenge is different from assessing the fitting error: it implicitly penalizes models that overfit the data. However, since most of the missing shells lie in between other shells (in terms of b values, TEs, etc.), the quality of signal extrapolation was not assessed. We get a glimpse of this from Figure 4, where the SSE is unevenly distributed between the b values. Here, the shell that bore the largest error is the b=1100 s/mm2 one; see also Figures 6 and 7. Of all ‘unseen’ shells, this shell combines the lowest Δ and highest |G|, placing it on the edge of the range of the measurement space sampled. Such a b‐value shell combines high signal magnitude with high sensitivity, i.e. the gradient of signal against b‐value is highest in this range, which makes it hard to predict. (We stress that this observation is in the context of the wider multi‐shell acquisition, and is not to be seen in isolation for its potential impact on single‐shell acquisition methods.) On the other hand, the variability of prediction errors in the b<750 s/mm2 range could arise from the varying sensitivity of different models to the free water component, which is challenging to estimate as it can easily be confounded with hindered water, or physiological effects, which are mostly observable in this low b‐value range. Future work can take this further, by selecting unseen shells outside the min–max range of experimental parameters. This is likely to penalize more complex models that overfit the data even more strongly.