The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment
The Benefits of the Matthews Correlation Coefficient (MCC) Over the Diagnostic Odds Ratio (DOR) in Binary Classification Assessment
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DOI：
10.1109/access.2021.3068614
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Jurman, Giuseppe
中科院分区：
文献类型：
--
作者：
Chicco, Davide;Starovoitov, Valery;Jurman, Giuseppe
为了评估二进制分类的质量，研究人员经常利用称为混淆矩阵的四条目列联表，该表包含真阳性、真阴性、假阳性和假阴性。为了在一个独特的分数中重述困惑矩阵的四个值，研究人员和统计学家开发了几个比率和衡量标准。在过去，几项科学研究已经表明，为什么马修斯相关系数(MCC)比混乱更具信息性和可信度--熵误差、准确度、F-1得分、庄家信息、标记和均衡准确度。在这项研究中，我们将MCC与诊断优势比(DOR)进行了比较，DOR是生物医学中有时使用的统计比率。在考察了MCC和DOR的性质之后，我们还利用一个称为混淆四面体的创新几何图形来描述它们之间的关系，这里首次提出了混淆四面体。然后，我们报告了一些MCC和DOR产生不一致结果的用例，并解释了为什么Matthews相关系数在两者之间更具信息性和可靠性。我们的结果可以在计算机科学和统计学中产生强大的影响，因为它们清楚地解释了为什么由马修斯相关系数提供的信息的可信度高于由诊断赔率比产生的可信度。
To assess the quality of a binary classification, researchers often take advantage of a four-entry contingency table called confusion matrix, containing true positives, true negatives, false positives, and false negatives. To recap the four values of a confusion matrix in a unique score, researchers and statisticians have developed several rates and metrics. In the past, several scientific studies already showed why the Matthews correlation coefficient (MCC) is more informative and trustworthy than confusion-entropy error, accuracy, F-1 score, bookmaker informedness, markedness, and balanced accuracy. In this study, we compare the MCC with the diagnostic odds ratio (DOR), a statistical rate employed sometimes in biomedical sciences. After examining the properties of the MCC and of the DOR, we describe the relationships between them, by also taking advantage of an innovative geometrical plot called confusion tetrahedron, presented here for the first time. We then report some use cases where the MCC and the DOR produce discordant outcomes, and explain why the Matthews correlation coefficient is more informative and reliable between the two. Our results can have a strong impact in computer science and statistics, because they clearly explain why the trustworthiness of the information provided by the Matthews correlation coefficient is higher than the one generated by the diagnostic odds ratio.