  Since 2008 August the Fermi Large Area Telescope (LAT) has provided
continuous coverage of the gamma-ray sky yielding more than 5000 gamma-ray
sources, but 54% of the detected sources remain with no certain or unknown
association with a low energy counterpart. Rigorous determination of class type
for a gamma-ray source requires the optical spectrum of the correct counterpart
but optical observations are demanding and time-consuming, then machine
learning techniques can be a powerful alternative for screening and ranking. We
use machine learning techniques to select blazar candidates among uncertain
sources characterized by gamma-ray properties very similar to those of Active
Galactic Nuclei. Consequently, the percentage of sources of uncertain type
drops from 54% to less than 12% predicting a new zoo for the Fermi gamma-ray
sources. The result of this study opens up new considerations on the population
of the gamma energy sky, and it will facilitate the planning of significant
samples for rigorous analysis and multi-wavelength observational campaigns.
