[R] Variables selection in Neural Networks
r_stat_solutions at hotmail.es
Sat Apr 26 11:59:42 CEST 2008
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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