  Identifying the magnetic state of materials is of great interest in a wide
range of applications, but direct identification is not always straightforward
due to limitations in neutron scattering experiments. In this work, we present
a machine-learning approach using decision-tree algorithms to identify
magnetism from the spin-integrated excitation spectrum, such as the density of
states. The dataset was generated by Hartree-Fock mean-field calculations of
candidate antiferromagnetic orders on a Wannier Hamiltonian, extracted from
first-principle calculations targeting BaOsO$_3$. Our machine learning model
was trained using various types of spectral data, including local density of
states, momentum-resolved density of states at high-symmetry points, and the
lowest excitation energies from the Fermi level. Although the density of states
shows good performance for machine learning, the broadening method had a
significant impact on the model's performance. We improved the model's
performance by designing the excitation energy as a feature for machine
learning, resulting in excellent classification of antiferromagnetic order,
even for test samples generated by different methods from the training samples
used for machine learning.
