Null QQ plots: A simple graphical alternative to significance testing for the comparison of classifiers

Daniel Berrar

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The evaluation of machine learning algorithms is commonly based on statistical significance tests. However, the suitability of such tests is often questionable. We propose null QQ plots as a simple yet powerful graphical alternative to significance testing. Using ten benchmark data sets, we demonstrate that these plots concisely summarize the essential results from a comparative classification study, while they are easy to produce and interpret.

Original languageEnglish
Title of host publicationICPR 2012 - 21st International Conference on Pattern Recognition
Pages1852-1855
Number of pages4
Publication statusPublished - 2012 Dec 1
Event21st International Conference on Pattern Recognition, ICPR 2012 - Tsukuba, Japan
Duration: 2012 Nov 112012 Nov 15

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Other

Other21st International Conference on Pattern Recognition, ICPR 2012
CountryJapan
CityTsukuba
Period12/11/1112/11/15

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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  • Cite this

    Berrar, D. (2012). Null QQ plots: A simple graphical alternative to significance testing for the comparison of classifiers. In ICPR 2012 - 21st International Conference on Pattern Recognition (pp. 1852-1855). [6460514] (Proceedings - International Conference on Pattern Recognition).