  Planetary-scale collisions are common during the last stages of formation of
solid planets, including the Solar system terrestrial planets. The problem of
growing planets has been divided into studying the gravitational interaction of
embryos relevant in million year timescales and treated with N-body codes and
the collision between objects with a timescale of hours to days and treated
with smoothed-particle hydrodynamics. These are now being coupled with simple
parameterized models. We set out to investigate if machine learning techniques
can offer a better solution by predicting the outcome of collisions which can
then be used in N-body simulations. We considered three different supervised
machine learning approaches: gradient boosting regression trees, nested models,
and gaussian processes. We found that the former produced the best results, and
that it was slightly surpassed by ensembling different algorithms. With
gaussian processes, we found the regions of parameter space that may yield the
most information to machine learning algorithms. Thus, we suggest SPH
calculations to focus first on mass ratios above 0.5.
