Re: [R] Variables selection in Neural Networks

From: Matthew Barber <>
Date: Sun, 27 Apr 2008 02:15:06 -0700 (PDT)

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_

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