  We demonstrate two sampling procedures assisted by machine learning models
via regression and classification. The main objective is the use of a neural
network to suggest points likely inside regions of interest, reducing the
number of evaluations of time consuming calculations. We compare results from
this approach with results from other sampling methods, namely Markov chain
Monte Carlo and MultiNest, obtaining results that range from comparably similar
to arguably better. In particular, we augment our classifier method with a
boosting technique that rapidly increases the efficiency within a few
iterations. We show results from our methods applied to a toy model and the
type II 2HDM, using 3 and 7 free parameters, respectively. The code used for
this paper and instructions are publicly available on the web.
