  Membership analysis is an important tool for studying star clusters. There
are various approaches to membership determination, including supervised and
unsupervised machine learning (ML) methods. We perform membership analysis
using the supervised machine learning approach. We train and test our ML models
on two sets of star cluster data: snapshots from $N$-body simulations and 21
different clusters from the Gaia Data Release 3 data. We explore five different
ML models: Random Forest (RF), Decision Trees, Support Vector Machines,
Feed-Forward Neural Networks, and K-Nearest Neighbors. We find that all models
produce similar results, with RF showing slightly better accuracy. We find that
a balance of classes in datasets is optional for successful learning. The
classification accuracy depends strongly on the astrometric parameters. The
addition of photometric parameters does not improve performance. We do not find
a strong correlation between the classification accuracy and clusters' age,
mass, and half-mass radius. At the same time, models trained on clusters with a
larger number of members generally produce better results.
