  Machine learning algorithms have been used to determine probabilistic
classifications of unassociated sources. Often classification into two large
classes, such as Galactic and extra-galactic, is considered. However, there are
many more physical classes of sources. For example, there are 23 classes in the
latest Fermi-LAT 4FGL-DR3 catalog. In this note we subdivide one of the large
classes into two subclasses in view of a more general multi-class
classification of gamma-ray sources. Each of the three large classes still
encompasses several of the physical classes. We compare the performance of
classifications into two and three classes. We calculate the receiver operating
characteristic curves for two-class classification, where in case of three
classes we sum the probabilities of the sub-classes in order to obtain the
class probabilities for the two large classes. We also compare precision,
recall, and reliability diagrams in the two- and three-class cases.
