There are two prediction competitions currently in the air.
- The Performance Prediction Challenge by Isabelle Guyon. Good entries minimize a weighted 0/1 loss + the difference between a prediction of this loss and the observed truth on 5 datasets. Isabelle tells me all of the problems are “real world” and the test datasets are large enough (17K minimum) that the winner should be well determined by ability rather than luck. This is due March 1.
- The Predictive Uncertainty Challenge by Gavin Cawley. Good entries minimize log loss on real valued output variables for one synthetic and 3 “real” datasets related to atmospheric prediction. The use of log loss (which can be infinite and hence is never convergent) and smaller test sets of size 1K to 7K examples makes the winner of this contest more luck dependent. Nevertheless, the contest may be of some interest particularly to the branch of learning (typically Bayes learning) which prefers to optimize log loss.
May the best predictor win.