Evidence-based model selection. Dependency of model evidence on a logarithmic scale from the number of features used and from the degree of non-linearity in the hidden layer. The values are average values over a 10-fold cross-validation procedure. The highest evidence is reached for models with 5 to 7 input features and 2 to 5 hidden neurons respectively. All these models have a low generalization error below 0.25.