
Abstract
Benchmark experiments produce data in a very specific format. The observations are drawn from the performance distributions of the candidate algorithms on resampled data sets. In this paper we introduce a comprehensive toolbox of exploratory and inferential analysis methods for benchmark experiments based on one or more data sets. We present new visualization techniques, show how formal non-parametric and parametric test procedures can be used to evaluate the results, and, finally, how to sum up to a statistically correct overall order of the candidate algorithms.
Item Type: | Paper |
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Keywords: | benchmark experiment, learning algorithm, visualisation, inference, ranking |
Faculties: | Mathematics, Computer Science and Statistics > Statistics > Technical Reports |
URN: | urn:nbn:de:bvb:19-epub-4134-6 |
Language: | English |
Item ID: | 4134 |
Date Deposited: | 02. Jun 2008, 07:44 |
Last Modified: | 04. Nov 2020, 12:47 |