  Extended stellar haloes are a natural by-product of the hierarchical
formation of massive galaxies. If merging is a non-negligible factor in the
growth of our Galaxy, evidence of such events should be encoded in its stellar
halo. Reliable identification of genuine halo stars is a challenging task
however. The 1st Gaia data release contains the positions, parallaxes and
proper motions for over 2 million stars, mostly in the Solar neighbourhood.
Gaia DR2 will enlarge this sample to over 1.5 billion stars, the brightest ~5
million of which will have a full phase-space information. Our aim is to
develop a machine learning model to reliably identify halo stars, even when
their full phase-space information is not available. We use the Gradient
Boosted Trees algorithm to build a supervised halo star classifier. The
classifier is trained on a sample extracted from the Gaia Universe Model
Snapshot, convolved with the errors of TGAS, as well as with the expected
uncertainties of the upcoming Gaia DR2. We also trained our classifier on the
cross-match between the TGAS and RAVE catalogues, where the halo stars are
labelled in an entirely model independent way. We then use this model to
identify halo stars in TGAS. When full phase- space information is available
and for Gaia DR2-like uncertainties, our classifier is able to recover 90% of
the halo stars with at most 30% distance errors, in a completely unseen test
set, and with negligible levels of contamination. When line-of-sight velocity
is not available, we recover ~60% of such halo stars, with less than 10%
contamination. When applied to the TGAS data, our classifier detects 337 high
confidence RGB halo stars. Although small, this number is consistent with the
expectation from models given the data uncertainties. The large parallax errors
are the biggest limitation to identify a larger number of halo stars in all the
cases studied.
