A more intuitive interpretation of the area under the ROC curve
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Abstract
The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being clinically irrelevant and lacking an intuitive interpretation. Most of the criticism is traced back to the fact that the ROC curve was introduced as the discriminative ability of a binary classifier across all its possible thresholds. Yet, this is not the curve’s only interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that the ROC plot is another way of presenting these risk distributions. This alternative perspective on the ROC plot invalidates most limitations of the AUC and attributes others to the underlying risk distributions. The separation of these distributions, represented by the area between the curves (AUC), is a more straightforward and intuitive measure of the inference of the discriminative ability of prediction models.
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2017. A more intuitive interpretation of the area under the ROC curve. PeerJ Preprints 5:e3468v1 https://doi.org/10.7287/peerj.preprints.3468v1Author comment
This is a preprint submission to PeerJ Preprints.
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Competing Interests
The authors declare that they have no competing interests
Author Contributions
A. Cecile J.W. Janssens conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Data Deposition
The following information was supplied regarding data availability:
The research in this article did not generate any data or code.
Funding
The authors received no funding for this work.
