The paper defines the OPS function that standardizes any CMBCP metric to its percentile rank in a reference distribution of possible performances, enabling consistent evaluation across different imbalance rates.
Domingos, A few useful things to know about machine learning, Communica- tions of the ACM 55 (10) (2012) 78–87
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Introducing the O-Value: A Universal Standardization for Confusion-Matrix-Based Classification Performance Metrics
The paper defines the OPS function that standardizes any CMBCP metric to its percentile rank in a reference distribution of possible performances, enabling consistent evaluation across different imbalance rates.