  We apply a number of statistical and machine learning techniques to classify
and rank gamma-ray sources from the Third Fermi Large Area Telescope (LAT)
Source Catalog (3FGL), according to their likelihood of falling into the two
major classes of gamma-ray emitters: pulsars (PSR) or Active Galactic Nuclei
(AGN). Using 1904 3FGL sources that have been identified/associated with AGN
(1738) and PSR (166), we train (using 70% of our sample) and test (using 30%)
our algorithms and find that the best overall accuracy (>96%) is obtained with
the Random Forest (RF) technique, while using a logistic regression (LR)
algorithm results in only marginally lower accuracy. We apply the same
techniques on a sub-sample of 142 known gamma-ray pulsars to classify them into
two major subcategories: young (YNG) and millisecond pulsars (MSP). Once more,
the RF algorithm has the best overall accuracy (~90%), while a boosted LR
analysis comes a close second. We apply our two best models (RF and LR) to the
entire 3FGL catalog, providing predictions on the likely nature of {\it
unassociated} sources, including the likely type of pulsar (YNG or MSP). We
also use our predictions to shed light on the possible nature of some gamma-ray
sources with known associations (e.g. binaries, SNR/PWN). Finally, we provide a
list of plausible X-ray counterparts for some pulsar candidates, obtained using
Swift, Chandra, and XMM. The results of our study will be of interest for both
in-depth follow-up searches (e.g. pulsar) at various wavelengths, as well as
for broader population studies.
