This approach leaves much to be desired. I hope that its practitioners start gauging it by the mean squared error of predicted probabilities.
Is the logic here is that low MSE of predicted probabilities equals a better calibrated model? What about discrimination? Perfect calibration implies perfect discrimination, but I often find that you can have two competing models, the first with higher discrimination (AUC) and worse calibration, and the the second the other way round. Which one is the better model?
-- Gad Abraham Dept. CSSE and NICTA The University of Melbourne Parkville 3010, Victoria, Australia email: [EMAIL PROTECTED] web: http://www.csse.unimelb.edu.au/~gabraham ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
