On Thu, Aug 19, 2010 at 10:56 AM, Watling,James I <watli...@ufl.edu> wrote: > Hi Steve-- > > Thanks for your interest in helping me figure this out. I think the problem > has to do with the values of the probabilities returned from the use of the > model to predict occurrence in a new dataframe.
Ok, so if you're sure this is the problem, and not, say, getting the correct values for the predictor variables at a given point, then I'd be a bit more thorough when building your model. Originally you said: > I have used a training dataset to train the model, and tested it against a > validation data set with good results: AUC is high, and the confusion matrix > indicates low commission and omission errors. Maybe your originally "good" AUC's was just a function of your train/test split? Why not use all of your data and do something like 10 fold cross validation to find: (1) Your average accuracy over your folds (2) The best value for your cost parameter; (how did you pick cost=10000)? (3) or even the best kernel to use. Doing 2 and 3 will likely be time consuming. To help with (2) you might try looking at the svmpath package: http://cran.r-project.org/web/packages/svmpath/index.html It only works on 2-class classification problems, and (I think) using a linear kernel (sorry, don't remember off hand, but it's written in the package help and linked pubs). You don't need to use svmpath, but then you'll need to define a "grid" of C values (or maybe a 2d grid, if your svm + kernel combo has more params) and train over these values ... takes lots of cpu time, but not too much human time. Does that make sense? -- Steve Lianoglou Graduate Student: Computational Systems Biology | Memorial Sloan-Kettering Cancer Center | Weill Medical College of Cornell University Contact Info: http://cbio.mskcc.org/~lianos/contact ______________________________________________ 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.
