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Estimating a smooth precision-recall curve

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Estimating a smooth precision-recall curve



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

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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.

Required Products Statistics and Machine Learning Toolbox
MATLAB release MATLAB 7.10 (R2010a)
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Comments and Ratings (4)
05 Feb 2017 Jan Motl

Jan Motl (view profile)

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

29 Nov 2012 sahbi sahboun

good works

Comment only
21 Nov 2011 Pedro

Pedro (view profile)

Very useful. Thank you.

18 Aug 2011 AMVR

AMVR (view profile)

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