Hi Fede,
You would have to eliminate the variables less correlated with the response variable. And for the explanatory variables to choose those that are very correlated to each other. I don't know if exists some function of R that does this by you. Mathew Barber _Fede_ wrote: > > Hi folks, > > I want to apply a neural network to a data set to classify the > observations in the different classes from a concrete response variable. > The idea is to prove different models from network modifying the number of > neurons of the hidden layer to control overfitting. But, to select the > best model how I can choose the relevant variables? How I can eliminate > those that are not significant for the model of neural networks? How I can > do this in R? I do this: > > dataset.nn=nnet(response.variable~., dataset, subset = training, size=1, > decay=0.001, linout=F, skip=T, maxit=200, Hess=T) > > What I am doing is to vary size between 0 and 1 since with a single layer > it can learn any type of function or continuous relation between a group > of input and output variables. But this only would give me two different > models. The ideal would be to reduce the model eliminating nonsignifictive > variables. How I can prove other different models? > > Regards. > > _Fede_ > -- View this message in context: http://www.nabble.com/Variables-selection-in-Neural-Networks-tp16911299p16921302.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org 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.
