Figure 4 also details the prediction error for different ranges of b values in the unseen dataset. Models inevitably vary in their prediction capabilities; some models perform better within a given b‐value range but are penalized more in another. Across the models, as the figure shows, the ranking between models was dominated by the signal prediction accuracy for b values between 750 and 1400s/mm2; specifically, the shell that has the largest weight on this error is the b=1100 s/mm2 one. The top‐ranking models, nevertheless, were better at predicting the signal for higher b‐value images as well. The prediction performance of lower b‐value images (<750s/mm2) in the genu was less consistent across ranks. For example, the models of Rokem and Sakaie outperformed most of the higher ranking models in this low b‐value range. The fornix is a more complex region than the genu, hence the performance across the shells is less consistent. In the fornix, the prediction errors were generally larger than in the genu across all b values for all models, except Rivera's, which showed the opposite effect. The prediction errors of the b=0 images were also larger than in the genu, especially for the highly ranked models of Poot and Ferizi. The prediction errors in other b‐value shells followed the overall ranking of the models more closely.