Considering the 'all cells' data split (Fig. ), it is evident that neural networks (FNNs and CNNs) outperformed classical models. In the absence of dropout, this is true in all cases (Fig. ); and it is also true in most cases using dropout (Fig. ). Dropout has the effect of decreasing a model's capacity by encouraging the learning of smoother functions. This smoothness constraint likely inhibits the learning of cell-specific patterns, which is a form of overfitting, and would explain the higher errors in Fig. ) compared to (b). In the absence of dropout, increasing the number of parameters or the depth of the networks produced lower errors. Hashed symbols represent a depth of 3 hidden layers, empty symbols a depth of 2 and filled symbols a depth of 1. It can be seen that for each feature configuration, deeper networks outperformed shallow networks with a similar number of parameters. This was not the case when using dropout; depth did not consistently reduce the test error, and the benefit from increasing the model capacity was limited. Both these effects are believed to be due to the regularising effect of dropout, which limited the learning of cell-specific patterns but without equivalent success in promoting the identification of more generic patterns.