You could look at
@Article{ e1071-papers:meyer+leisch+hornik:2003,
author = {David Meyer and Friedrich Leisch and Kurt Hornik},
title = {The Support Vector Machine under Test},
journal = {Neurocomputing},
year = 2003,
month = {September},
pages = {169--186},
volume = 55
}
which compares a lot of classifiction and regression methods available
in R. The purpose obviously was to assess SVMs, but the error rates can
be compared independently from that. Generally, the performance of
nnet() was acceptable, but ensemble methods have been quite competitive
as well.
Best,
David
---
I was wandering if anybody ever tried to compare the classification
accuracy of nnet to other (rpart, tree, bagging) models. From what I
know, there is no reason to expect a significant difference in
classification accuracy between these models, yet in my particular case
I get about 10% error rate for tree, rpart and bagging model and 80%
error rate for nnet, applied to the same data.
Thanks.
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