In this work we aim to provide machine learning practitioners with tools to answer the question: is there class-conditional flipping noise in my labels? In particular, we present hypothesis tests to reliably check whether a given dataset of instance-label pairs has been corrupted with class-conditional label noise. While previous works explore the direct estimation of the noise rates, this is known to be hard in practice and does not offer a real understanding of how trustworthy the estimates are. These methods typically require anchor points - examples whose true posterior is either 0 or 1. Differently, in this paper we assume we have access to a set of anchor points whose true posterior is approximately 1/2. The proposed hypothesis tests are built upon the asymptotic properties of Maximum Likelihood Estimators for Logistic Regression models and accurately distinguish the presence of class-conditional noise from uniform noise. We establish the main properties of the tests, including a theoretical and empirical analysis of the dependence of the power on the test on the training sample size, the number of anchor points, the difference of the noise rates and the use of realistic relaxed anchors.

中文翻译：

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分类条件标签噪声的统计假设检验

在这项工作中，我们旨在为机器学习从业人员提供回答以下问题的工具：我的标签中是否存在类条件的翻转噪声？特别是，我们提出了假设检验，以可靠地检查实例标签对的给定数据集是否已因类条件标签噪声而损坏。尽管先前的工作探索了噪声速率的直接估计，但是在实践中很难做到这一点，并且无法真正了解估计的可信度。这些方法通常需要锚点-真实后验值为0或1的示例。不同地，在本文中，我们假设我们可以访问一组真实后验大约为1/2的锚定点。所提出的假设检验建立在Logistic回归模型的最大似然估计器的渐近性质的基础上，并准确区分类条件噪声和均匀噪声的存在。我们建立了测试的主要属性，包括对功率对测试的依赖性，训练样本大小，锚点数，噪声率的差异以及使用实际的宽松锚点的理论和经验分析。