[R] Variables selection in Neural Networks

From: _Fede_ <r_stat_solutions_at_hotmail.es>
Date: Sat, 26 Apr 2008 02:59:42 -0700 (PDT)

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?



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Received on Sat 26 Apr 2008 - 10:08:34 GMT

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