论文标题
使用未标记数据的二进制分类器的混淆矩阵和精度统计信息：诊断测试方法
Confusion Matrices and Accuracy Statistics for Binary Classifiers Using Unlabeled Data: The Diagnostic Test Approach
论文作者
论文摘要
医学研究人员解决了估计二进制医学诊断测试的敏感性和特异性的问题，而没有黄金标准测试进行比较。这个问题与在未标记数据上估算分类器的混淆矩阵相同。本文介绍了如何修改诊断测试解决方案，以估计无标记数据上有监督或无监督的二进制分类器的混淆矩阵和准确性统计信息。
Medical researchers have solved the problem of estimating the sensitivity and specificity of binary medical diagnostic tests without gold standard tests for comparison. That problem is the same as estimating confusion matrices for classifiers on unlabeled data. This article describes how to modify the diagnostic test solutions to estimate confusion matrices and accuracy statistics for supervised or unsupervised binary classifiers on unlabeled data.
