well, how to do you know which ones are the best out of several hundreds? I will average all results out of several hundreds.
On 5/7/07, hadley wickham <[EMAIL PROTECTED]> wrote: > On 5/6/07, nathaniel Grey <[EMAIL PROTECTED]> wrote: > > Hello R-Users, > > > > I have been using (nnet) by Ripley to train a neural net on a test > > dataset, I have obtained predictions for a validtion dataset using: > > > > PP<-predict(nnetobject,validationdata) > > > > Using PP I can find the -2 log likelihood for the validation datset. > > > > However what I really want to know is how well my nueral net is doing at > > classifying my binary output variable. I am new to R and I can't figure out > > how you can assess the success rates of predictions. > > > > table(PP, binaryvariable) > should get you started. > > Also if you're using nnet with random starts, I strongly suggest > taking the best out of several hundred (or maybe thousand) trials - it > makes a big difference! > > Hadley > > ______________________________________________ > [email protected] 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. > -- WenSui Liu A lousy statistician who happens to know a little programming (http://spaces.msn.com/statcompute/blog) ______________________________________________ [email protected] 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.
