  Identifying X-ray binary (XRB) candidates in nearby galaxies requires
distinguishing them from possible contaminants including foreground stars and
background active galactic nuclei. This work investigates the use of supervised
machine learning algorithms to identify high-probability X-ray binary
candidates. Using a catalogue of 943 Chandra X-ray sources in the Andromeda
galaxy, we trained and tested several classification algorithms using the X-ray
properties of 163 sources with previously known types. Amongst the algorithms
tested, we find that random forest classifiers give the best performance and
work better in a binary classification (XRB/non-XRB) context compared to the
use of multiple classes. Evaluating our method by comparing with
classifications from visible-light and hard X-ray observations as part of the
Panchromatic Hubble Andromeda Treasury, we find compatibility at the 90% level,
although we caution that the number of sources in common is rather small. The
estimated probability that an object is an X-ray binary agrees well between the
random forest binary and multiclass approaches and we find that the
classifications with the highest confidence are in the X-ray binary class. The
most discriminating X-ray bands for classification are the 1.7-2.8, 0.5-1.0,
2.0-4.0, and 2.0-7.0 keV photon flux ratios. Of the 780 unclassified sources in
the Andromeda catalogue, we identify 16 new high-probability X-ray binary
candidates and tabulate their properties for follow-up.
