I would recommend reading the following: Dietterich, T. G., (1998).
Approximate Statistical Tests for Comparing Supervised Classification
Learning Algorithms. Neural Computation, 10 (7) 1895-1924.
http://web.engr.oregonstate.edu/~tgd/publications/index.html
The issues in comparing methods are
Leave-one-out is very inaccurate for some methods, notably trees, but fine
for some others (e.g. LDA) if used with a good measure of accuracy.
Hint: there is a very large literature on this, so read any good book on
classification to find out what is known.
On Tue, 6 Jan 2004, Christoph Lehmann
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
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