  We study the problem of reconstruction of high-energy cosmic rays mass
composition from the experimental data of extensive air showers. We develop
several machine learning methods for the reconstruction of energy spectra of
separate primary nuclei at energies 1-100 PeV, using the public data and
Monte-Carlo simulations of the KASCADE experiment from the KCDC platform. We
estimate the uncertainties of our methods, including the unfolding procedure,
and show that the overall accuracy exceeds that of the method used in the
original studies of the KASCADE experiment.
