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Precision-Recall and ROC Curves

version (4.14 KB) by Stefan Schroedl
Calculate and plot P/R and ROC curves for binary classification tasks.


Updated 17 Mar 2010

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Consider a binary classification task, and a real-valued predictor, where higher values denote more confidence that an instance is positive. By setting a fixed threshold on the output, we can trade-off recall (=true positive rate) versus false positive rate (resp. precision).

Depending on the relative class frequencies, ROC and P/R curves can highlight different properties; for details, see e.g., Davis & Goadrich, 'The Relationship Between Precision-Recall and ROC Curves', ICML 2006.

Cite As

Stefan Schroedl (2022). Precision-Recall and ROC Curves (, MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2007a
Compatible with any release
Platform Compatibility
Windows macOS Linux

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