  We utilize machine learning methods to distinguish BL Lacertae objects (BL
Lac) from Flat Spectrum Radio Quasars (FSRQ) within a sample of likely X-ray
blazar counterparts to Fermi 3FGL unassociated gamma-ray sources. From our
previous work, we have extracted 84 sources that were classified as $\geq$ 99%
likley to be blazars. We then utilize Swift$-$XRT, Fermi, and WISE (The
Wide-field Infrared Survey Explorer) data together to distinguish the specific
type of blazar, FSRQs or BL Lacs. Various X-ray and Gamma-ray parameters can be
used to differentiate between these subclasses. These are also known to occupy
different parameter space on the WISE color-color diagram. Using all these data
together would provide more robust results for the classified sources. We
utilized a Random Forest Classifier to calculate the probability for each
blazar to be associated with a BL Lac or an FSRQ. Based on P$_{bll}$, which is
the probability for each source to be a BL Lac, we placed our sources into five
different categories based on this value as follows; P$_{bll}$ $\geq$ 99%:
highly likely BL Lac, P$_{bll}$ $\geq$ 90%: likely BL Lac, P$_{bll}$ $\leq$ 1%:
highly likely FSRQ, P$_{bll}$ $\leq$ 10%: likely FSRQ, and 90% $<$ P$_{bll}$
$<$ 10%: ambiguous. Our results categorize the 84 blazar candidates as 50
likely BL Lacs and the rest 34 being ambiguous. A small subset of these sources
have been listed as associated sources in the most recent Fermi catalog, 4FGL,
and in these cases our results are in agreement on the classification.
