BACKGROUND To 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 F1 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. RESULTS The 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. CONCLUSIONS In this article, we show how MCC produces a more informative and truthful score in evaluating binary classifications than accuracy and F1 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 F1 score in evaluating binary classification tasks by all scientific communities.

中文翻译：

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马修斯相关系数（MCC）优于F1分数的优势和二元分类评估的准确性。

背景技术为了评估二元分类及其混淆矩阵，科学研究人员可以根据他们正在研究的实验目标，采用几种统计率。尽管在机器学习中是一个至关重要的问题，但对于统一的选修措施尚未达成广泛共识。在混淆分类矩阵上计算出的准确性和F1分数已经（并且仍然是）在二元分类任务中最受欢迎的度量标准之中。但是，这些统计方法可能会危险地显示过度乐观的膨胀结果，尤其是在不平衡的数据集上。结果马修斯相关系数（MCC）是一种更可靠的统计率，只有在预测在所有四个混淆矩阵类别中均获得良好结果时，才产生高分（真阳性，错误否定，正确否定和错误肯定），与数据集中正向元素的大小和负向元素的大小成正比。结论在本文中，我们将通过在六个合成用例和实际基因组学场景中首先解释数学属性，然后说明MCC的资产，来说明MCC在评估二元分类时如何产生比准确度和F1分数更高的信息性和真实性得分。 。我们认为，在评估所有科学界的二元分类任务时，应优先选择准确性和F1分数作为Matthews相关系数。我们将通过首先解释数学属性，然后在六个合成用例和实际基因组学场景中说明MCC的资产，来展示MCC在评估二元分类时如何产生比准确性和F1分数更高的信息性和真实性的分数。我们认为，在评估所有科学界的二元分类任务时，应优先选择准确性和F1分数作为Matthews相关系数。我们将通过首先解释数学属性，然后在六个合成用例和实际基因组学场景中说明MCC的资产，来展示MCC在评估二元分类时如何产生比准确性和F1分数更高的信息性和真实性的分数。我们认为，在评估所有科学界的二元分类任务时，应优先选择准确性和F1分数作为Matthews相关系数。