Hi all,
there is tune() in the e1071 package for doing this in general, and,
among others, a tune.nnet() wrapper (see ?tune):
> tmodel = tune.nnet(Species ~ ., data = iris, size = 1:5)
> summary(tmodel)
Parameter tuning of `nnet':
- sampling method: 10-fold cross validation
- best parameters:
size
1
- best performance: 0.01333333
- Detailed performance results:
size error dispersion
1 1 0.01333333 0.02810913
2 2 0.02666667 0.04661373
3 3 0.02666667 0.04661373
4 4 0.02000000 0.04499657
5 5 0.02666667 0.04661373
> plot(tmodel)
> tmodel$best.model
a 4-1-3 network with 11 weights
inputs: Sepal.Length Sepal.Width Petal.Length Petal.Width
output(s): Species
options were - softmax modelling
etc.
Best
David
On 7/23/07, S.O. Nyangoma <[EMAIL PROTECTED]> wrote:
> > Hi
> > It clear that to do a classification with svm under 10-fold cross
> > validation one uses
> >
> > svm(Xm, newlabs, type = "C-classification", kernel = "linear",cross =
> > 10)
> >
> > What corresponds to the nnet?
> > nnet(.....,cross=10)?
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