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Commonly used software tools produce conflicting and overly-optimistic AUPRC values.

Wenyu ChenChen MiaoZhenghao ZhangCathy Sin-Hang FungRan WangYizhen ChenYan QianLixin ChengKevin Y YipStephen Kwok-Wing TsuiQin Cao
Published in: bioRxiv : the preprint server for biology (2024)
The precision-recall curve (PRC) and the area under it (AUPRC) are useful for quantifying classification performance. They are commonly used in situations with imbalanced classes, such as cancer diagnosis and cell type annotation. We evaluated 10 popular tools for plotting PRC and computing AUPRC, which were collectively used in > 3,000 published studies. We found the AUPRC values computed by the tools rank classifiers differently and some tools produce overly-optimistic results.
Keyphrases
  • machine learning
  • deep learning
  • systematic review
  • randomized controlled trial
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  • single cell
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