When faced with severely imbalanced binary classification problems, we often train models on bootstrapped data in which the number of instances of each class occur in a more favorable ratio, often equal to one. We view algorithmic inequity through the lens of imbalanced classification: In order to balance the performance of a classifier across groups, we can bootstrap to achieve training sets that are balanced with respect to both labels and group identity. For an example problem with severe class imbalance—prediction of suicide death from administrative patient records—we illustrate how an equity-directed bootstrap can bring test set sensitivities and specificities much closer to satisfying the equal odds criterion. In the context of naïve Bayes and logistic regression, we analyse the equity-weighted bootstrap, demonstrating that it works by bringing odds ratios close to one, and linking it to methods involving intercept adjustment, thresholding, and weighting.

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股权加权自举：示例和分析

当面对严重不平衡的二元分类问题时，我们经常在自举数据上训练模型，其中每个类的实例数以更有利的比例出现，通常等于一。我们从不平衡分类的角度来看待算法的不公平性：为了平衡分类器跨组的性能，我们可以引导以实现在标签和组身份方面均平衡的训练集。对于具有严重类别不平衡的示例问题（从管理患者记录中预测自杀死亡），我们说明了公平导向的引导程序如何使测试集的敏感性和特异性更接近于满足等概率标准。在朴素贝叶斯和逻辑回归的背景下，我们分析了权益加权引导程序，