  Conditional density estimation (CDE) models can be useful for many
statistical applications, especially because the full conditional density is
estimated instead of traditional regression point estimates, revealing more
information about the uncertainty of the random variable of interest. In this
paper, we propose a new methodology called Odds Conditional Density Estimator
(OCDE) to estimate conditional densities in a supervised learning scheme. The
main idea is that it is very difficult to estimate $p_{x,y}$ and $p_{x}$ in
order to estimate the conditional density $p_{y|x}$, but by introducing an
instrumental distribution, we transform the CDE problem into a problem of odds
estimation, or similarly, training a binary probabilistic classifier. We
demonstrate how OCDE works using simulated data and then test its performance
against other known state-of-the-art CDE methods in real data. Overall, OCDE is
competitive compared with these methods in real datasets.
