期刊
BMC GENOMICS
卷 21, 期 1, 页码 -出版社
BMC
DOI: 10.1186/s12864-019-6413-7
关键词
Matthews correlation coefficient; Binary classification; F-1 score; Confusion matrices; Machine learning; Biostatistics; Accuracy; Dataset imbalance; Genomics
BackgroundTo evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F-1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets.ResultsThe Matthews correlation coefficient (MCC), instead, is a more reliable statistical rate which produces a high score only if the prediction obtained good results in all of the four confusion matrix categories (true positives, false negatives, true negatives, and false positives), proportionally both to the size of positive elements and the size of negative elements in the dataset.ConclusionsIn this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F-1 score, by first explaining the mathematical properties, and then the asset of MCC in six synthetic use cases and in a real genomics scenario. We believe that the Matthews correlation coefficient should be preferred to accuracy and F-1 score in evaluating binary classification tasks by all scientific communities.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据