  State of the art exoplanet transit surveys are producing ever increasing
quantities of data. To make the best use of this resource, in detecting
interesting planetary systems or in determining accurate planetary population
statistics, requires new automated methods. Here we describe a machine learning
algorithm that forms an integral part of the pipeline for the NGTS transit
survey, demonstrating the efficacy of machine learning in selecting planetary
candidates from multi-night ground based survey data. Our method uses a
combination of random forests and self-organising-maps to rank planetary
candidates, achieving an AUC score of 97.6\% in ranking 12368 injected planets
against 27496 false positives in the NGTS data. We build on past examples by
using injected transit signals to form a training set, a necessary development
for applying similar methods to upcoming surveys. We also make the
\texttt{autovet} code used to implement the algorithm publicly accessible.
\texttt{autovet} is designed to perform machine learned vetting of planetary
candidates, and can utilise a variety of methods. The apparent robustness of
machine learning techniques, whether on space-based or the qualitatively
different ground-based data, highlights their importance to future surveys such
as TESS and PLATO and the need to better understand their advantages and
pitfalls in an exoplanetary context.
