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ROC and AUC with a Binary Predictor: a Potentially Misleading Metric.

John Muschelli1

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205.

Journal of Classification
|November 30, 2020
PubMed
Summary

Linear interpolation in Receiver Operating Characteristic (ROC) curve analysis with binary predictors can lead to misleading Area Under the Curve (AUC) results. Reporting the interpolation method used is recommended for accurate model performance assessment.

Keywords:
Rarea under the curveaucroc

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Area of Science:

  • Statistics
  • Machine Learning
  • Data Science

Background:

  • Receiver Operating Characteristic (ROC) curves are vital for evaluating binary classification model performance.
  • Area Under the Curve (AUC) summarizes ROC curve performance but can be sensitive to predictor types.
  • Binary predictors present unique challenges for ROC curve interpretation due to limited thresholds.

Purpose of the Study:

  • To investigate the impact of interpolation methods on AUC calculations for binary predictors.
  • To compare AUC results obtained from different statistical software packages.
  • To provide recommendations for accurate reporting of model performance metrics.

Main Methods:

  • Analysis of ROC curves with a focus on binary predictors.
  • Comparison of linear interpolation versus step function interpolation.
  • Evaluation of AUC calculation implementations in R, Python, Stata, and SAS.

Main Results:

  • Linear interpolation, commonly used in software, can significantly alter AUC values for binary predictors.
  • Different software implementations yield varying AUC results due to interpolation differences.
  • The step function interpolator offers a more conservative and potentially less misleading AUC estimate.

Conclusions:

  • The choice of interpolation method critically affects AUC interpretation for binary predictors.
  • Users should be aware of and report the interpolation method used in AUC calculations.
  • The step function (pessimistic) approach is recommended for more robust AUC estimation.