  The efficient classification of different types of supernova is one of the
most important problems for observational cosmology. However, spectroscopic
confirmation of most objects in upcoming photometric surveys, such as the The
Rubin Observatory Legacy Survey of Space and Time (LSST), will be unfeasible.
The development of automated classification processes based on photometry has
thus become crucial. In this paper we investigate the performance of machine
learning (ML) classification on the final cosmological constraints using
simulated lightcurves from The Supernova Photometric Classification Challenge,
released in 2010. We study the use of different feature sets for the
lightcurves and many different ML pipelines based on either decision tree
ensembles or automated search processes. To construct the final catalogs we
propose a threshold selection method, by employing a \emph{Bias-Variance
tradeoff}. This is a very robust and efficient way to minimize the Mean Squared
Error. With this method we were able to get very strong cosmological
constraints, which allowed us to keep $\sim 75\%$ of the total information in
the type Ia SNe when using the SALT2 feature set and $\sim 33\%$ for the other
cases (based on either the Newling model or on standard wavelet decomposition).
