Hy,
I try to give an example of overfitting with multi-layer perceptron.
I have done following small example :
library(nnet)
set.seed(1)
x <- matrix(rnorm(20),10,2)
z <- matrix(rnorm(10),10,1)
rx <- max(x)-min(x)
rz <- max(z)-min(z)
x <- x/rx
z <- z/rz
erreur <- 10^9
for(i in 1:100){
temp.mod <- nnet(x=x,y=z,size=10,rang=1,maxit=1000)
if(temp.mod$value<erreur){
res.mod <- temp.mod
erreur <- res.mod$value
}
}
cat("\nFinal error : ",res.mod$value,"\n")
Normaly it is easy task for an MLP with 10 hidden units to reduce the
final error to almost 0 (althougt there is nothing to predict).
But the smallest error that I get is : 0.753895 (very poor result)
Maybe this problem is already known??
Maybe the fault is mine but I don't see where.
Joseph Rynkiewicz
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