Hello,
I am attempting to use elasticnet to classify a number of documents.
The features are words. The data is coded into a matrix with each document as
a row and each word as a column. The data is binary, with {0,1} indicating the
presence of a word.
I want to use the cross validation function of elasticnet (cv.enet). However,
when the code selects a random subset of the data for a given run, some of the
word columns may be all 0. (A given word simply isn't present in the subset of
data sampled.) This causes the the function to return an error about variance
of 0.
Any suggestions on how to mitigate this issue? Given that I want a 5-fold
cross validation to determine optimal tuning?
Thanks!
--
Noah Silverman, M.S.
UCLA Department of Statistics
8117 Math Sciences Building
Los Angeles, CA 90095
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