[R] Neural Nets (nnet) - evaluating success rate of predictions
liuwensui at gmail.com
Mon May 7 13:28:06 CEST 2007
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 <h.wickham at gmail.com> wrote:
> On 5/6/07, nathaniel Grey <nathaniel.grey at yahoo.co.uk> 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!
> R-help at stat.math.ethz.ch mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
A lousy statistician who happens to know a little programming
More information about the R-help