While all the other parameters push it to a higher value, thickness and fiber concentration being the most relevant ones for this test example. Going further, we can replicate this analysis for all the samples in the respective test sets and evaluated with the respective NNs. If this information is plotted together (Figure ), we get an overview of the influence of each feature in the NNs predictions. In these stochastically generated GDL datasets we get that the influence of the features both in pre-trained and TL approach NN are in the same order of importance, i.e. there is not a significant change in the relative importance of each feature to the others when the predictions are made. As can be seen in Figure , the order of the importance of the features is the following one: thickness, fiber concentration, binder concentration, fiber diameter and compression. Thickness is the most relevant one and compression is the least relevant one in determining the geometrical tortuosity of the GDL microstructure datasets with our pre-trained and TL approach NNs.