Hi,
I use nnet for my classification problem and have a problem concerning the
calculation of the final value for my validation data.(nnet only calculates the
final value for the training data). I made my own final value formula (for the
training data I get the same value as nnet):
# prob-matrix
pmatrix <- cat*fittedValues
tmp <- rowSums(pmatrix)
# -log likelihood
finalValue <- sum(-log(tmp))
# add penalty term
finalValue + sum(decay * weights^2)
where cat is a matrix with cols for each possible category and a row for each
data record. The values are 1 for the target categories of a data record and 0
otherwise.
My problem is, that I get Inf-values for some validation data records, because
the rowsum of cat*fittedValues gets 0 and the log gets Inf.
Has anyone an idea how to deal with that problem properly? How does nnet?
I´m thinking of a penalty value for those values. That means if
cat*fittedValues == 0 not to calculate the log but add e.g. 100 instead of
"-log(tmp)" to the finalValue-sum??
But how to determine the penalty value???
I´m looking forwar for all suggestions,
Andrea.
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