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

hadley wickham h.wickham at gmail.com
Mon May 7 11:22:48 CEST 2007

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!


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