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? 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 two different models. The ideal would be to be reducing 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-tp16911299p16911299.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.
