  Recently, the learning by confusion (LBC) approach has been proposed as a
machine learning tool to determine the critical temperature Tc of phase
transitions without any prior knowledge of its even approximate value. However,
the effectiveness of the method has been demonstrated only for continuous phase
transitions, where confusion can result only from a deliberate incorrect
labeling of the data and not from the coexistence of different phases. To
verify whether the confusion scheme can also be used for discontinuous phase
transitions, in this work, we apply the LBC method to three microscopic models,
the Blume-Capel, the q-state Potts, and the Falicov-Kimball models, which
undergo continuous or discontinuous phase transitions depending on model
parameters. With the help of a simple model, we predict that the phase
coexistence present in discontinuous phase transitions can make the neural
network more confused and thus decrease its performance. However, numerical
calculations performed for the models mentioned above indicate that other
aspects of this kind of phase transition are more important and can render the
LBC method less effective. Nevertheless, we demonstrate that in some cases the
same aspects allow us to use the LBC method to identify the order of a phase
transition
