Equity-Directed Bootstrapping: Examples and Analysis

• URL: http://arxiv.org/abs/2108.06624v1
• Date: Sat, 14 Aug 2021 22:09:27 GMT
• Title: Equity-Directed Bootstrapping: Examples and Analysis
• Authors: Harish S. Bhat and Majerle E. Reeves and Sidra Goldman-Mellor
• Abstract summary: We show how an equity-directed bootstrap can bring test set sensitivities and specificities closer to satisfying the equal odds criterion. In the context of na"ive Bayes and logistic regression, we analyze the equity-directed bootstrap, demonstrating that it works by bringing odds ratios close to one.
• Score: 3.007949058551534
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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, e.g., 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 na\"ive Bayes and logistic regression, we analyze the equity-directed 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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