[R] Variables selection in Neural Networks
c__programming at hotmail.com
Sun Apr 27 11:15:06 CEST 2008
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.
> 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?
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.
More information about the R-help