Calibrating for Class Weights by Modeling Machine Learning

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Tác giả: Andrew Caplin, Daniel Martin, Philip Marx

Ngôn ngữ: eng

Ký hiệu phân loại: 006.31 Machine learning

Thông tin xuất bản: 2022

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Bộ sưu tập: Metadata

ID: 195108

A much studied issue is the extent to which the confidence scores provided by machine learning algorithms are calibrated to ground truth probabilities. Our starting point is that calibration is seemingly incompatible with class weighting, a technique often employed when one class is less common (class imbalance) or with the hope of achieving some external objective (cost-sensitive learning). We provide a model-based explanation for this incompatibility and use our anthropomorphic model to generate a simple method of recovering likelihoods from an algorithm that is miscalibrated due to class weighting. We validate this approach in the binary pneumonia detection task of Rajpurkar, Irvin, Zhu, et al. (2017).Comment: 14 pages, 4 figures
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