论文标题
扩展F1度量，概率方法
Extending F1 metric, probabilistic approach
论文作者
论文摘要
本文探讨了用于评估二进制分类器性能的众所周知的F1分数的扩展。我们使用精确，召回，特异性和负预测价值的概率解释提出了新的度量。我们描述其特性并将其与常见指标进行比较。然后，我们在混淆矩阵的边缘案例中证明了其行为。最后，在实际数据集上训练的二进制分类器上测试了公制的属性。
This article explores the extension of well-known F1 score used for assessing the performance of binary classifiers. We propose the new metric using probabilistic interpretation of precision, recall, specificity, and negative predictive value. We describe its properties and compare it to common metrics. Then we demonstrate its behavior in edge cases of the confusion matrix. Finally, the properties of the metric are tested on binary classifier trained on the real dataset.
