  Context: Given the current big data era in Astronomy, machine learning based
methods have being applied over the last years to identify or classify objects
like quasars, galaxies and stars from full sky photometric surveys. Aims: Here
we systematically evaluate the performance of Random Forests (RF) in
classifying quasars using either magnitudes or colours, both from broad and
narrow-band filters, as features. Methods: The working data consists of
photometry from the ALHAMBRA Gold Catalogue that we cross-matched with the
Sloan Digital Sky Survey (SDSS) and with the Million Quasars Catalogue
(Milliquas) for objects labelled as quasars, galaxies or stars. A RF classifier
is trained and tested to evaluate the effect on final accuracy and precision of
varying the free parameters and the effect of using narrow or broad-band
magnitudes or colours. Results: Best performances of the classifier yielded
global accuracy and quasar precision around 0.9. Varying model free parameters
(within reasonable ranges of values) has no significant effects on the final
classification. Using colours instead of magnitudes as features results in
better performances of the classifier, especially using colours from the
ALHAMBRA Survey. Colours that contribute the most to the classification are
those containing the near-infrared $JHK$ bands.
