[R] Neural Nets (nnet) - evaluating success rate of predictions

[hadley wickham]

Pick the one with the lowest error rate on your training data?
Hadley

On 5/7/07, Wensui Liu <liuwensui at gmail.com> wrote:
> 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!
> >
> > Hadley
> >
> > ______________________________________________
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> >
>
>
> --
> WenSui Liu
> A lousy statistician who happens to know a little programming
> (http://spaces.msn.com/statcompute/blog)
>