  Directional detection is a promising Dark Matter search strategy. Even though
it could accommodate to a sizeable background contamination, electron/recoil
discrimination remains a key and challenging issue as for direction-insensitive
detectors. The measurement of the 3D track may be used to discriminate
electrons from nuclear recoils. While a high rejection power is expected above
20 keV ionization, a dedicated data analysis is needed at low energy. After
identifying discriminant observables, a multivariate analysis, namely a Boosted
Decision Tree, is proposed, enabling an efficient event tagging for Dark Matter
search. We show that it allows us to optimize rejection while keeping a rather
high efficiency which is compulsory for rare event search.With respect to a
sequential analysis, the rejection is about 20 times higher with a multivariate
analysis, for the same Dark Matter exclusion limit.
