The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets.

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 int

SUBMITTER: Saito T 

PROVIDER: S-EPMC4349800 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature