Abstract Confusion matrices and their associated statistics are a well established tool in machine learning to evaluate the accuracy of a classifier. In the present study, we define a rough confusion matrix based on a very general classifier, and derive various statistics from it which are related to common rough set estimators. In other words, we perform a rough set–like analysis on a confusion matrix, which is the converse of the usual procedure; in particular, we consider upper approximations. A suitable index for measuring the tightness of the upper bound uses a ratio of odds. Odds ratios offer a symmetric interpretation of lower and upper precision, and remove the bias in the upper approximation. We investigate rough odds ratios of the parameters obtained from the confusion matrix; to guard against undue random influences, we also approximate their standard errors.

中文翻译：

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粗糙集逼近的指数及其在混淆矩阵中的应用

摘要 混淆矩阵及其相关统计数据是机器学习中用于评估分类器准确性的完善工具。在本研究中，我们基于一个非常通用的分类器定义了一个粗糙的混淆矩阵，并从中导出与常见的粗糙集估计器相关的各种统计数据。换句话说，我们对混淆矩阵执行类似粗糙集的分析，这是通常过程的逆过程；特别是，我们考虑上近似。衡量上限紧密度的合适指标使用比值比。优势比提供了下精度和上精度的对称解释，并消除了上近似中的偏差。我们研究了从混淆矩阵获得的参数的粗略优势比；防止不适当的随机影响，