Reproduce “Precision-Recall Curve (PRC) Classification Trees” on OpenResearch
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About this paper
Jiaju Miao, Wei Zhu
The authors introduce PRC trees for imbalanced classification, selecting each split’s feature by area under the precision–recall curve and its cutoff by the F1 score, the harmonic mean of precision and recall. On the credit-default test partition, PRC random forest reaches F1 0.5432, versus 0.4701 for standard and 0.4968 for weighted random forest; results do not establish universal superiority.
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