  We present results on the identification of phase transitions in
ferrimagnetic GdFeCo alloys using machine learning. The approach for finding
phase transitions in the system is based on the `learning by confusion' scheme,
which allows one to characterize phase transitions using a universal $W$-shape.
By applying the `learning by confusion' scheme, we obtain 2D $W$-a shaped
surface that characterizes a triple phase transition point of the GdFeCo alloy.
We demonstrate that our results are in the perfect agreement with the procedure
of the numerical minimization of the thermodynamical potential, yet our
machine-learning-based scheme has the potential to provide a speedup in the
task of the phase transition identification.
