File Exchange

image thumbnail

Estimating a smooth precision-recall curve

version 1.0 (9.36 KB) by

A set of MATLAB functions for computing a smooth approximation to the precision-recall curve.

3 Ratings



View License

In binary classification, the precision-recall curve (PRC) has become a widespread conceptual tool for assessing classification performance. The curve relates the positive predictive value of a classifier to its true positive rate and provides a useful alternative to the well-known receiver operating characteristic (ROC). A smooth estimate of the PRC can be computed on the basis of a simple distributional assumption about the underlying decision values.

This archive contains an easy-to-use MATLAB implementation of this approach.

For full details, see:

K.H. Brodersen, C.S. Ong, K.E. Stephan, J.M. Buhmann (2010)
The binormal assumption on precision-recall curves.
Proceedings of the 20th International Conference on Pattern Recognition (ICPR), 4263-4266.

Comments and Ratings (4)

Jan Motl

Jan Motl (view profile)

Good. Just add a check at the input of prc_conthist for input vectors being rows, not columns.

sahbi sahboun

good works


Pedro (view profile)

Very useful. Thank you.


AMVR (view profile)

MATLAB Release
MATLAB 7.10 (R2010a)

Download apps, toolboxes, and other File Exchange content using Add-On Explorer in MATLAB.

» Watch video