  We study the characteristic probability density distribution of random flat
band models by machine learning. The models considered here are constructed on
the basis of the molecular-orbital representation, which guarantees the
existence of the macroscopically degenerate zero-energy modes even in the
presence of randomness. We find that flat band states are successfully
distinguished from conventional extended and localized states, indicating the
characteristic feature of the flat band states. We also find that the flat band
states can be detected when the target data are defined in the different
lattice from the training data, which implies the universal feature of the flat
band states constructed by the molecular-orbital representation.
