  The consecutive steps of cascade decay initiated by H to tau tau can be
useful for the measurement of Higgs couplings and in particular of the Higgs
boson parity. In the previous papers we have found, that multi-dimensional
signatures of the tau^pm to pi^pm pi^0 nu and tau^pm to 3pi^pm nu decays can be
used to distinguish between scalar and pseudoscalar Higgs state. The Machine
Learning techniques (ML) of binary classification, offered break-through
opportunities to manage such complex multidimensional signatures.
  The classification between two possible CP states: scalar and pseudoscalar,
is now extended to the measurement of the hypothetical mixing angle of Higgs
boson parity states. The functional dependence of H to tau tau matrix element
on the mixing angle is predicted by theory. The potential to determine
preferred mixing angle of the Higgs boson events sample including $\tau$-decays
is studied using Deep Neural Network. The problem is adressed as classification
or regression with the aim to determine the per-event: a) probability
distribution (spin weight) of the mixing angle; b) parameters of the functional
form of the spin weight; c) the most preferred mixing angle.
  Performance of methods are evaluated and compared. Numerical results are
collected.
