Unachievable region in precision-recall space and its effect on empirical evaluation
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Boyd, K; Costa, VS; Davis, J; Page, CD
Published in: Proceedings of the 29th International Conference on Machine Learning, ICML 2012
October 10, 2012
Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of PR space that is completely unachievable, and the size of this region depends only on the skew. This paper precisely characterizes the size of that region and discusses its implications for empirical evaluation methodology in machine learning. Copyright 2012 by the author(s)/owner(s).
Duke Scholars
Published In
Proceedings of the 29th International Conference on Machine Learning, ICML 2012
Publication Date
October 10, 2012
Volume
1
Start / End Page
639 / 646
Citation
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Boyd, K., Costa, V. S., Davis, J., & Page, C. D. (2012). Unachievable region in precision-recall space and its effect on empirical evaluation. In Proceedings of the 29th International Conference on Machine Learning, ICML 2012 (Vol. 1, pp. 639–646).
Boyd, K., V. S. Costa, J. Davis, and C. D. Page. “Unachievable region in precision-recall space and its effect on empirical evaluation.” In Proceedings of the 29th International Conference on Machine Learning, ICML 2012, 1:639–46, 2012.
Boyd K, Costa VS, Davis J, Page CD. Unachievable region in precision-recall space and its effect on empirical evaluation. In: Proceedings of the 29th International Conference on Machine Learning, ICML 2012. 2012. p. 639–46.
Boyd, K., et al. “Unachievable region in precision-recall space and its effect on empirical evaluation.” Proceedings of the 29th International Conference on Machine Learning, ICML 2012, vol. 1, 2012, pp. 639–46.
Boyd K, Costa VS, Davis J, Page CD. Unachievable region in precision-recall space and its effect on empirical evaluation. Proceedings of the 29th International Conference on Machine Learning, ICML 2012. 2012. p. 639–646.
Published In
Proceedings of the 29th International Conference on Machine Learning, ICML 2012
Publication Date
October 10, 2012
Volume
1
Start / End Page
639 / 646