[R] Variables selection in Neural Networks

[_Fede_]

Thanks to answer Matthew. I hope that there is some function because to do
all that by hand with so many variables it can be horrible. In addition, how
it would make to see those that are not correlated and those that yes. Which
is the threshold to decide one or another thing?

_Fede_


Matthew Barber wrote:
> 
> 
> 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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