The issues in comparing methods are subtle and difficult. With such a small data set I would be a little surprised if you could get any result that are truly statistically significant, especially if your goal is to compare among good non-linear methods (i.e., in which there are unlikely to huge differences because of model misspecification). However, because the issues are subtle, it is easy to get results that appear significant...
hope this helps,
Tony Plate
At Tuesday 04:31 PM 1/6/2004 +0100, Christoph Lehmann wrote:
Hi what would you recommend to compare classification methods such as LDA, classification trees (rpart), bagging, SVM, etc:
10-fold cv (as in Ripley p. 346f)
or
leaving-one-out (as e.g. implemented in LDA)?
my data-set is not that huge (roughly 200 entries)
many thanks for a hint
Christoph -- Christoph Lehmann <[EMAIL PROTECTED]>
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Tony Plate [EMAIL PROTECTED]
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