  We discuss a methodology of machine learning to deduce the neutron star
equation of state from a set of mass-radius observational data. We propose an
efficient procedure to deal with a mapping from finite data points with
observational errors onto an equation of state. We generate training data and
optimize the neural network. Using independent validation data (mock
observational data) we confirm that the equation of state is correctly
reconstructed with precision surpassing observational errors. We finally
discuss the relation between our method and Bayesian analysis with an emphasis
put on generality of our method for underdetermined problems.
