  We applied a variety of parametric and non-parametric machine learning models
to predict the probability distribution of rainfall based on 1M training
examples over a single year across several U.S. states. Our top performing
model based on a squared loss objective was a cross-validated parametric
k-nearest-neighbor predictor that took about six days to compute, and was
competitive in a world-wide competition.
