A Closer Look at AUROC and AUPRC under Class Imbalance (AI summary)

Key Points

1. The paper discusses the instability in clinical risk stratification models using deep learning, highlighting the potential drawbacks of using ROC curves and suggesting the use of Precision-Recall curves for more accurate model evaluation.

2. The authors propose a method for sampling a set of model scores and labels that will have a target AUROC, offering a procedure for optimizing overall AUROC and AUPRC, providing insights into model behavior under varying conditions.

3. Through a comprehensive literature search and AI-assisted screening, the paper identifies 128 relevant papers that discuss the claim that "AUPRC is better than AUROC in cases of class imbalance."

4. The authors employ an AI-assisted approach, utilizing models such as GPT-3.5 and GPT-4.0 Turbo, to analyze and refine a large dataset of papers on the comparative effectiveness of AUPRC over AUROC in scenarios of class imbalance.

5. The paper presents a keyword-driven filtering process, a code availability section, and comprehensive lists of identified papers and extracted quotes for collaborative analysis.

6. The paper provides a detailed methodology for data acquisition, keyword-driven filtering, AI-assisted screening and refinement, manual review, and code availability related to the Arxiv search.

7. It discusses analytical models and simulation results to demonstrate the impact of optimizing for overall AUROC and AUPRC under varying conditions, offering insights into the behavior of these metrics.

8. The paper touches on the use of machine learning models for violence detection, knee osteoarthritis progression prediction, outlier detection, and drug-drug interaction prediction, emphasizing the importance of evaluating model performance with appropriate metrics.

9. The study offers a comprehensive review of literature, an analytical model to derive equations for ROC and PR curves, and simulation results to understand the implications of optimizing for overall AUROC and AUPRC.

Summary

Reevaluating the AUPRC in Binary Classification Tasks
The paper challenges the widely accepted belief that the area under the precision-recall curve (AUPRC) is superior to the area under the receiver operating characteristic (AUROC) for binary classification tasks with class imbalance. The authors provide a novel mathematical analysis showing the interrelation between AUROC and AUPRC in probabilistic terms and demonstrate that AUPRC is not superior in cases of class imbalance. The paper emphasizes that AUPRC can inadvertently favor model improvements in subpopulations with more frequent positive labels, which can heighten algorithmic disparities. It also points out that AUPRC is affected by the large number of true negatives, making it "less optimistic" than AUROC in scenarios of low prevalence.

The study further discusses the implications of the prevalence dependence of AUPRC and how it can lead to fairness concerns in domains like healthcare. The authors also conducted a comprehensive review of existing machine learning literature, revealing significant deficits in empirical backing and a trend of misattributions that have fueled the widespread acceptance of AUPRC’s supposed advantages.

The paper concludes by advocating for a more thoughtful and context-aware approach to selecting evaluation metrics in machine learning, emphasizing the need for a balanced and conscientious approach to metric selection.

Reference: https://arxiv.org/abs/2401.06091