Area under precision-recall curves for weighted and unweighted data.

ABSTRACT: Precision-recall curves are highly informative about the performance of binary classifiers, and the area under these curves is a popular scalar performance measure for comparing different classifiers. However, for many applications class labels are not provided with absolute certainty, but with some degree of confidence, often reflected by weights or soft labels assigned to data points. Computing the area under the precision-recall curve requires interpolating between adjacent supporting points, but previous interpolation schemes are not directly applicable to weighted data. Hence, even in cases where weights were available, they had to be neglected for assessing classifiers using precision-recall curves. Here, we propose an interpolation for precision-recall curves that can also be used f

SUBMITTER: Keilwagen J 

PROVIDER: S-EPMC3961324 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature