  The neutrino closure method is often used to obtain kinematics of
semileptonic decays with one unreconstructed particle. The kinematics of decays
can be deducted by a two-fold ambiguity with a quadratic equation. To resolve
the two-fold ambiguity, a new method based on Machine Learning (ML) is
proposed. We study the effect of different sets of features and regressors on
the improvement of reconstructed invariant mass squared of $\ell \nu$
system~($q^2$). The result shows that the best performance is obtained by using
the flight vector as the features, and the multilayer perceptron (MLP) model as
the regressor. Compared with the random choice, the MLP model improves the
resolution of reconstructed $q^2$ by $\sim$40\%. Furthermore, the possibility
of using this method on various semileptonic decays is shown.
