  The Fermi gamma-ray space telescope has revolutionized our view of the
gamma-ray sky and the high energy processes in the Universe. While the number
of known gamma-ray emitters has increased by orders of magnitude since the
launch of Fermi, there is an ever increasing number of, now more than a
thousand, detected point sources whose low-energy counterpart is to this day
unknown. To address this problem, we combined optical polarization measurements
from the RoboPol survey as well as other discriminants of blazars from publicly
available all-sky surveys in machine learning (random forest and logistic
regression) frameworks that could be used to identify blazars in the Fermi
unidentified fields with an accuracy of >95%. Out of the potential
observational biases considered, blazar variability seems to have the most
significant effect reducing the predictive power of the frameworks to ~80-85%.
We apply our machine learning framework to six unidentified Fermi fields
observed using the RoboPol polarimeter. We identified the same candidate source
proposed by Mandarakas et al. for 3FGL J0221.2+2518.
