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The receiver operating characteristic curve accurately assesses imbalanced datasets.

Eve Richardson1, Raphael Trevizani1,2, Jason A Greenbaum1

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The receiver operating characteristic (ROC) curve is robust for evaluating imbalanced datasets in biology, contrary to common belief. The precision-recall (PR) curve is highly sensitive to class imbalance, making ROC analysis more reliable for "needle in a haystack" problems.

Keywords:
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Area of Science:

  • Machine learning
  • Bioinformatics
  • Computational biology

Background:

  • Class imbalance is common in biological data, posing challenges for predictive modeling.
  • The receiver operating characteristic (ROC) curve and precision-recall (PR) curve are common metrics for evaluating binary classifiers.
  • Misconceptions exist regarding the suitability of ROC and PR curves for imbalanced datasets.

Purpose of the Study:

  • To clarify the behavior of ROC and PR curves under class imbalance.
  • To determine the robustness of ROC analysis versus PR analysis in imbalanced biological data.
  • To investigate the relationship between class imbalance and classifier performance metrics.

Main Methods:

  • Simulations were conducted to model class imbalance scenarios.
  • A real-world biological case study was analyzed.
  • Performance was evaluated using ROC and PR curves and their associated areas under the curve (AUC).

Main Results:

  • The ROC curve is robust to class imbalance, maintaining reliable performance evaluation.
  • The PR curve is highly sensitive to class imbalance, potentially misrepresenting classifier performance.
  • Disentangling classifier performance from class imbalance in PR-AUC is challenging.

Conclusions:

  • ROC analysis is a suitable and robust method for evaluating binary classifiers in the presence of significant class imbalance in biological data.
  • PR curve analysis can be misleading with imbalanced data, requiring careful interpretation.
  • Understanding the impact of class imbalance on evaluation metrics is crucial for accurate biological data analysis.