On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
Paper summary This paper describes common pitfalls when classifiers are compared and recommends McNemars test ## Notes * t-test is simply the wrong test for such an experimental design ## See also * Prechelt "A quantitative study of experimental evaluations of neural network algorithms" - most of 200 evaluated paper had flaws * Wolpert "On the connection between in-sample testing and generalization error" - No classifier is always better than another one. * Diettrich: [Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms](http://www.shortscience.org/paper?bibtexKey=journals/neco/Dietterich98) * Demsar: [Statistical Comparisons of Classifiers over Multiple Data Sets](http://www.shortscience.org/paper?bibtexKey=demvsar2006statistical)
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On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach
Salzberg, Steven
Data Min. Knowl. Discov. - 1997 via Bibsonomy
Keywords: dblp


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