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The class-imbalance issue is intrinsic to many real-world machine learning tasks, particularly to the rare-event classification problems. Although the impact and treatment of imbalanced data is widely known, the magnitude of a metric&#x2019;s sensitivity to class imbalance has attracted little attention. As a result, often the sensitive metrics are dismissed while their sensitivity may only be marginal. In this paper, we introduce an intuitive evaluation framework that quantifies metrics&#x2019; sensitivity to the class imbalance. Moreover, we reveal an interesting fact that there is a logarithmic behavior in metrics&#x2019; sensitivity meaning that the higher imbalance ratios are associated with the lower sensitivity of metrics. Our framework builds an intuitive understanding of the class-imbalance impact on metrics. We believe this can help avoid many common mistakes, specially the less-emphasized and incorrect assumption that all metrics&#x2019; quantities are comparable under different class-imbalance ratios.

Measuring Class-Imbalance Sensitivity of Deterministic Performance Evaluation Metrics