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A Closer Look at AUROC and AUPRC under Class Imbalance
The paper challenges the belief that the area under the precision-recall curve (AUPRC) is a superior metric for model comparison in machine learning. It argues that AUPRC can be biased and highlights a lack of empirical evidence supporting its supposed advantages.
https://arxiv.org/abs//2401.06091
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
The paper challenges the belief that the area under the precision-recall curve (AUPRC) is a superior metric for model comparison in machine learning. It argues that AUPRC can be biased and highlights a lack of empirical evidence supporting its supposed advantages.
https://arxiv.org/abs//2401.06091
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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