One more experiment with the sports science data clearly shows the issue of overfitting of the neural network. I devised a script that automatically generates networks with two hidden layers of neurons and varies the layers‘ sizes from 2 to 16 systematically, respectively. No matter how the parameters for the learning process were set, the mean squared error plotted against the two layer sizes (right: first hidden layer, left: second hidden layer) is zero as soon as the networks get larger:
Without cross validation, the error is zero with larger networks

However, when applying cross-validation (test set: 1 record, validation set: 1 record, training set: 38 records), the error, especially towards the larger layer sizes, rises. This is a clear sign of overfitting (scales as in the last figure):
With cross validation, one can see the effects of overfitting quite clearly

As usual, the matlab script for this entry, which doesn’t differ much from the latest ones: neuro10-new_plot.m.