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(Psycho-)analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithms

Author

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  • Eugster, Manuel J.A.
  • Leisch, Friedrich
  • Strobl, Carolin

Abstract

It is common knowledge that the performance of different learning algorithms depends on certain characteristics of the data—such as dimensionality, linear separability or sample size. However, formally investigating this relationship in an objective and reproducible way is not trivial. A new formal framework for describing the relationship between data set characteristics and the performance of different learning algorithms is proposed. The framework combines the advantages of benchmark experiments with the formal description of data set characteristics by means of statistical and information-theoretic measures and with the recursive partitioning of Bradley–Terry models for comparing the algorithms’ performances. The formal aspects of each component are introduced and illustrated by means of an artificial example. Its real-world usage is demonstrated with an application example consisting of thirteen widely-used data sets and six common learning algorithms. The Appendix provides information on the implementation and the usage of the framework within the R language.

Suggested Citation

  • Eugster, Manuel J.A. & Leisch, Friedrich & Strobl, Carolin, 2014. "(Psycho-)analysis of benchmark experiments: A formal framework for investigating the relationship between data sets and learning algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 986-1000.
    • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:986-1000
    DOI: 10.1016/j.csda.2013.08.007
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    References listed on IDEAS

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    1. Achim Zeileis & Kurt Hornik, 2007. "Generalized M‐fluctuation tests for parameter instability," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 488-508, November.
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    4. Carolin Strobl & Florian Wickelmaier & Achim Zeileis, 2011. "Accounting for Individual Differences in Bradley-Terry Models by Means of Recursive Partitioning," Journal of Educational and Behavioral Statistics, , vol. 36(2), pages 135-153, April.
    5. Givens, G.H. & Beveridge, J.R. & Phillips, P.J. & Draper, B. & Lui, Y.M. & Bolme, D., 2013. "Introduction to face recognition and evaluation of algorithm performance," Computational Statistics & Data Analysis, Elsevier, vol. 67(C), pages 236-247.
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    Cited by:

    1. Anne-Laure Boulesteix & Robert Hable & Sabine Lauer & Manuel J. A. Eugster, 2015. "A Statistical Framework for Hypothesis Testing in Real Data Comparison Studies," The American Statistician, Taylor & Francis Journals, vol. 69(3), pages 201-212, August.

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