  Over 30% of the ~4000 known exoplanets to date have been discovered using
'validation', where the statistical likelihood of a transit arising from a
false positive (FP), non-planetary scenario is calculated. For the large
majority of these validated planets calculations were performed using the vespa
algorithm (Morton et al. 2016). Regardless of the strengths and weaknesses of
vespa, it is highly desirable for the catalogue of known planets not to be
dependent on a single method. We demonstrate the use of machine learning
algorithms, specifically a gaussian process classifier (GPC) reinforced by
other models, to perform probabilistic planet validation incorporating prior
probabilities for possible FP scenarios. The GPC can attain a mean log-loss per
sample of 0.54 when separating confirmed planets from FPs in the Kepler
threshold crossing event (TCE) catalogue. Our models can validate thousands of
unseen candidates in seconds once applicable vetting metrics are calculated,
and can be adapted to work with the active TESS mission, where the large number
of observed targets necessitates the use of automated algorithms. We discuss
the limitations and caveats of this methodology, and after accounting for
possible failure modes newly validate 50 Kepler candidates as planets, sanity
checking the validations by confirming them with vespa using up to date stellar
information. Concerning discrepancies with vespa arise for many other
candidates, which typically resolve in favour of our models. Given such issues,
we caution against using single-method planet validation with either method
until the discrepancies are fully understood.
