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Unachievable region in precision-recall space and its effect on empirical evaluation.
| Content Provider | CiteSeerX |
|---|---|
| Author | Boyd, Kendrick Costa, Vítor Santos |
| Abstract | 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. 1. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Unachievable Region Precision-recall Space Empirical Evaluation Roc Curve Class Skew Pr Space Machine Learning Data Set Empirical Evaluation Methodology Class Skew Change |
| Content Type | Text |
| Resource Type | Article |
