平均精度与ROC曲线下面积的关系

Proceedings of the 2015 International Conference on The Theory of Information Retrieval Pub Date : 2015-09-27 DOI:10.1145/2808194.2809481

Wanhua Su, Yan Yuan, Mu Zhu

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引用次数: 94

摘要

对于类似的评估任务，接受者工作特征曲线下的面积(AUC)通常被研究人员用于机器学习，而平均精度(AP)则被信息检索界更多地使用。我们建立了一些结果来解释为什么会出现这种情况。具体来说，我们表明，当AUC和AP都被重新缩放到[0,1]中时，AP大约是AUC乘以系统的初始精度。

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A Relationship between the Average Precision and the Area Under the ROC Curve

For similar evaluation tasks, the area under the receiver operating characteristic curve (AUC) is often used by researchers in machine learning, whereas the average precision (AP) is used more often by the information retrieval community. We establish some results to explain why this is the case. Specifically, we show that, when both the AUC and the AP are rescaled to lie in [0,1], the AP is approximately the AUC times the initial precision of the system.

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Proceedings of the 2015 International Conference on The Theory of Information Retrieval

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