2006 TheRelationshipBetweenPrecision

• (Davis & Goadrich, 2006) ⇒ Jesse Davis, and Mark Goadrich. (2006). “The Relationship Between Precision-Recall and ROC Curves.” In: Proceedings of the 23rd International Conference on Machine learning. ISBN:1-59593-383-2 doi:10.1145/1143844.1143874

Subject Headings: ROC Space, PR Space.

Cited By

• http://scholar.google.com/scholar?q=%222006%22+The+Relationship+Between+Precision-Recall+and+ROC+Curves
• http://dl.acm.org/citation.cfm?id=1143844.1143874&preflayout=flat#citedby

Quotes

Abstract

Receiver Operator Characteristic (ROC) curves are commonly used to present results for binary decision problems in machine learning. However, when dealing with highly skewed datasets, Precision-Recall (PR) curves give a more informative picture of an algorithm's performance. We show that a deep connection exists between ROC space and PR space, such that a curve dominates in ROC space if and only if it dominates in PR space. A corollary is the notion of an achievable PR curve, which has properties much like the convex hull in ROC space; we show an efficient algorithm for computing this curve. Finally, we also note differences in the two types of curves are significant for algorithm design. For example, in PR space it is incorrect to linearly interpolate between points. Furthermore, algorithms that optimize the area under the ROC curve are not guaranteed to optimize the area under the PR curve.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2006 TheRelationshipBetweenPrecision	Jesse Davis Mark Goadrich			The Relationship Between Precision-Recall and ROC Curves				10.1145/1143844.1143874		2006