  We present three different methods to estimate error bars on the predictions
made using a neural network. All of them represent lower bounds for the
extrapolation errors. For example, we did not include an analysis on robustness
against small perturbations of the input data.
  At first, we illustrate the methods through a simple toy model, then, we
apply them to some realistic cases related to nuclear masses. By using
theoretical data simulated either with a liquid-drop model or a Skyrme energy
density functional, we benchmark the extrapolation performance of the neural
network in regions of the Segr\`e chart far away from the ones used for the
training and validation. Finally, we discuss how error bars can help
identifying when the extrapolation becomes too uncertain and thus unreliable
