ROC曲线好，但精度-调出曲线差 [英] Good ROC curve but poor precision-recall curve

问题描述

我有一些不太了解的机器学习结果.我正在使用python sciki-learn，具有2个以上的14个功能的百万个数据. 精确度"曲线上"ab"的分类看起来很差，但是Ab的ROC看起来和大多数其他组的分类一样好.有什么可以解释的?

I have some machine learning results that I don't quite understand. I am using python sciki-learn, with 2+ million data of about 14 features. The classification of 'ab' looks pretty bad on the precision-recall curve, but the ROC for Ab looks just as good as most other groups' classification. What can explain that?

推荐答案

类不平衡.

与ROC曲线不同，PR曲线对不平衡非常敏感.如果针对不平衡数据优化分类器以获得良好的AUC，则很可能会获得较差的精度调用结果.

Unlike the ROC curve, PR curves are very sensitive to imbalance. If you optimize your classifier for good AUC on an unbalanced data you are likely to obtain poor precision-recall results.

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