%0 Journal Article %1 saito2015precisionrecall %A Saito, Takaya %A Rehmsmeier, Marc %D 2015 %I Public Library of Science %J PLOS ONE %K auc classification evaluation learning machine precision recall roc %N 3 %P 1--21 %R 10.1371/journal.pone.0118432 %T The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets %U https://doi.org/10.1371/journal.pone.0118432 %V 10 %X Binary classifiers are routinely evaluated with performance measures such as sensitivity and specificity, and performance is frequently illustrated with Receiver Operating Characteristics (ROC) plots. Alternative measures such as positive predictive value (PPV) and the associated Precision/Recall (PRC) plots are used less frequently. Many bioinformatics studies develop and evaluate classifiers that are to be applied to strongly imbalanced datasets in which the number of negatives outweighs the number of positives significantly. While ROC plots are visually appealing and provide an overview of a classifier's performance across a wide range of specificities, one can ask whether ROC plots could be misleading when applied in imbalanced classification scenarios. We show here that the visual interpretability of ROC plots in the context of imbalanced datasets can be deceptive with respect to conclusions about the reliability of classification performance, owing to an intuitive but wrong interpretation of specificity. PRC plots, on the other hand, can provide the viewer with an accurate prediction of future classification performance due to the fact that they evaluate the fraction of true positives among positive predictions. Our findings have potential implications for the interpretation of a large number of studies that use ROC plots on imbalanced datasets.

@article{saito2015precisionrecall, abstract = {Binary classifiers are routinely evaluated with performance measures such as sensitivity and specificity, and performance is frequently illustrated with Receiver Operating Characteristics (ROC) plots. Alternative measures such as positive predictive value (PPV) and the associated Precision/Recall (PRC) plots are used less frequently. Many bioinformatics studies develop and evaluate classifiers that are to be applied to strongly imbalanced datasets in which the number of negatives outweighs the number of positives significantly. While ROC plots are visually appealing and provide an overview of a classifier's performance across a wide range of specificities, one can ask whether ROC plots could be misleading when applied in imbalanced classification scenarios. We show here that the visual interpretability of ROC plots in the context of imbalanced datasets can be deceptive with respect to conclusions about the reliability of classification performance, owing to an intuitive but wrong interpretation of specificity. PRC plots, on the other hand, can provide the viewer with an accurate prediction of future classification performance due to the fact that they evaluate the fraction of true positives among positive predictions. Our findings have potential implications for the interpretation of a large number of studies that use ROC plots on imbalanced datasets.}, added-at = {2017-09-11T15:06:16.000+0200}, author = {Saito, Takaya and Rehmsmeier, Marc}, biburl = {https://www.bibsonomy.org/bibtex/2267493795ec5fbb57d7b014f92b47d06/jaeschke}, doi = {10.1371/journal.pone.0118432}, interhash = {143b79f7511f261c5f73b718fa20f32d}, intrahash = {267493795ec5fbb57d7b014f92b47d06}, journal = {PLOS ONE}, keywords = {auc classification evaluation learning machine precision recall roc}, month = {03}, number = 3, pages = {1--21}, publisher = {Public Library of Science}, timestamp = {2017-09-11T15:06:16.000+0200}, title = {The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets}, url = {https://doi.org/10.1371/journal.pone.0118432}, volume = 10, year = 2015 }