  We conduct X-ray spectral fits on 184 likely counterparts to Fermi-LAT 3FGL
unassociated sources. Characterization and classification of these sources
allows for more complete population studies of the high-energy sky. Most of
these X-ray spectra are well fit by an absorbed power law model, as expected
for a population dominated by blazars and pulsars. A small subset of 7 X-ray
sources have spectra unlike the power law expected from a blazar or pulsar and
may be linked to coincident stars or background emission. We develop a
multiwavelength machine learning classifier to categorize unassociated sources
into pulsars and blazars using gamma- and X-ray observations. Training a random
forest procedure with known pulsars and blazars, we achieve a cross-validated
classification accuracy of 98.6%. Applying the random forest routine to the
unassociated sources returned 126 likely blazar candidates (defined as $
P_{bzr} > 90 \% $) and 5 likely pulsar candidates ($ P_{bzr} < 10 \% $). Our
new X-ray spectral analysis does not drastically alter the random forest
classifications of these sources compared to previous works, but it builds a
more robust classification scheme and highlights the importance of X-ray
spectral fitting. Our procedure can be further expanded with UV, visual, or
radio spectral parameters or by measuring flux variability.
