  The latest $\textit{Fermi}$-LAT gamma-ray catalog, 4FGL-DR3, presents a large
fraction of sources without clear association to known counterparts, i.e.,
unidentified sources (unIDs). In this paper, we aim to classify them using
machine learning algorithms, which are trained with the spectral
characteristics of associated sources to predict the class of the unID
population. With the state-of-the-art $\texttt{CatBoost}$ algorithm, based on
gradient boosting decision trees, we are able to reach a 67% accuracy on a
23-class dataset. Removing a single of these classes -- blazars of uncertain
type -- increases the accuracy to 81%. If interested only in a binary
AGN/pulsar distinction, the model accuracy is boosted up to 99%. Additionally,
we perform an unsupervised search among both known and unID population, and try
to predict the number of clusters of similar sources, without prior knowledge
of their classes. The full code used to perform all calculations is provided as
an interactive Python notebook.
