  A large number of unidentified sources found by astronomical surveys and
other observations necessitate the use of an automated classification technique
based on machine learning methods. The aim of this paper is to find a suitable
automated classifier to identify the point X-ray sources in the Chandra Source
Catalogue (CSC) 2.0 in the categories of active galactic nuclei (AGN), X-ray
emitting stars, young stellar objects (YSOs), high-mass X-ray binaries (HMXBs),
low-mass X-ray binaries (LMXBs), ultra luminous X-ray sources (ULXs),
cataclysmic variables (CVs), and pulsars. The catalogue consists of approx
3,17,000 sources, out of which we select 2,77,069 point sources based on the
quality flags available in CSC 2.0. In order to identify unknown sources of CSC
2.0, we use multi-wavelength features, such as magnitudes in optical/UV bands
from Gaia-EDR3, SDSS and GALEX, and magnitudes in IR bands from 2MASS, WISE and
MIPS-Spitzer, in addition to X-ray features (flux and variability) from CSC
2.0. We find the Light Gradient Boosted Machine, an advanced decision
tree-based machine learning classification algorithm, suitable for our purpose
and achieve $93\%$ precision, $93\%$ recall score and 0.91 Mathew's Correlation
coefficient score. With the trained classifier, we identified 54,770 (14,066)
sources with more than $3{\sigma}$ (4${\sigma}$) confidence, out of which there
are 32,600 (8,574) AGNs, 16,148 (5,166) stars, 5,184 (208) YSOs, 439 (46)
HMXBs, 197 (71) LMXBs, 50 (0) ULXs, 89 (1) CVs, and 63 (0) pulsars. This method
can also be useful for identifying sources of other catalogues reliably.
