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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced

Takaya Saito1, Marc Rehmsmeier1

  • 1Computational Biology Unit, Department of Informatics, University of Bergen, P. O. Box 7803, N-5020, Bergen, Norway.

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Summary

Receiver Operating Characteristics (ROC) plots can be misleading for imbalanced datasets in bioinformatics. Precision/Recall (PRC) plots offer a more accurate assessment of classifier performance on imbalanced data.

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Area of Science:

  • Bioinformatics
  • Machine Learning
  • Data Science

Background:

  • Binary classifiers are commonly evaluated using metrics like sensitivity and specificity.
  • Receiver Operating Characteristics (ROC) plots are frequently used to visualize classifier performance.
  • Precision/Recall (PRC) plots, based on positive predictive value (PPV), are less common alternatives.

Purpose of the Study:

  • To investigate the potential misleading nature of ROC plots when applied to imbalanced datasets.
  • To compare the interpretability and reliability of ROC plots versus PRC plots for imbalanced classification.
  • To highlight the implications for bioinformatics studies using ROC plots on imbalanced data.

Main Methods:

  • Analysis of classifier performance metrics (sensitivity, specificity, PPV).
  • Evaluation using Receiver Operating Characteristics (ROC) plots.
  • Evaluation using Precision/Recall Curves (PRC) plots.
  • Assessment in the context of strongly imbalanced datasets.

Main Results:

  • ROC plots can be deceptively interpreted on imbalanced datasets due to a misinterpretation of specificity.
  • PRC plots provide a more accurate prediction of future classification performance.
  • The fraction of true positives among positive predictions is key for accurate assessment.

Conclusions:

  • ROC plots may lead to incorrect conclusions about classifier reliability in imbalanced scenarios.
  • PRC plots are recommended for a more accurate evaluation of classifiers on imbalanced datasets.
  • Re-evaluation of studies using ROC plots on imbalanced data is suggested.