  Supervised learning algorithms take as input a set of labelled examples and
return as output a predictive model. Such models are used to estimate labels
for future, previously unseen examples drawn from the same generating
distribution. In this paper we investigate the possibility of using supervised
learning to estimate the dimension of a non-Markovian quantum environment. Our
approach uses an ensemble learning method, the Random Forest Regressor, applied
to classically simulated data sets. Our results indicate this is a promising
line of research.
