Publication: (Psycho-)analysis of benchmark experiments : a formal framework for investigating the relationship between data sets and learning algorithms
(Psycho-)analysis of benchmark experiments : a formal framework for investigating the relationship between data sets and learning algorithms
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Eugster, M. J. A., Leisch, F., & Strobl, C. (2014). (Psycho-)analysis of benchmark experiments : a formal framework for investigating the relationship between data sets and learning algorithms. Computational Statistics & Data Analysis, 71, 986–1000. https://doi.org/10.1016/j.csda.2013.08.007
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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
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- Eugster, Manuel J A
- Leisch, Friedrich
- Strobl, Carolin
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Eugster, M. J. A., Leisch, F., & Strobl, C. (2014). (Psycho-)analysis of benchmark experiments : a formal framework for investigating the relationship between data sets and learning algorithms. Computational Statistics & Data Analysis, 71, 986–1000. https://doi.org/10.1016/j.csda.2013.08.007