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Precision-recall curve

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A precision-recall curve is a plot of the precision (positive predictive value, y-axis) against the recall (sensitivity, x-axis) for different thresholds. It is an alternative for the ROC curve (Saito & Rehmsmeier, 2015).

MedCalc generates the precision-recall curve from the raw data (not from a sensitivity-PPV table).

How to enter data for a precision-recall curve

To create a precision-recall curve you should have a measurement of interest (= the parameter you want to study) and an independent diagnosis which classifies your study subjects into two distinct groups: a diseased and non-diseased group. The latter diagnosis should be independent from the measurement of interest.

In the spreadsheet, create a column Classification and a column for the variable of interest, e.g. Param. For every study subject enter a code for the classification as follows: 1 for the diseased cases, and 0 for the non-diseased or normal cases. In the Param column, enter the measurement of interest (this can be measurements, grades, etc. - if the data are categorical, code them with numerical values).

Data for precision-recall curve

Required input

Dialog box for Precision-recall curve


Results for Precision-recall curve

MedCalc reports:

See also a note on Criterion values.


Precision-recall curve

When the option to mark points corresponding to criterion values in the graph was selected, then when you click on a marker, the corresponding criterion (for positivity) will be given together with recall (sensitivity), precision (positive predictive value) and F1 score.

Precision-recall curve


See also

External links

Recommended book

Book cover

An Introduction to the Bootstrap
Bradley Efron, R.J. Tibshirani

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Statistics is a subject of many uses and surprisingly few effective practitioners. The traditional road to statistical knowledge is blocked, for most, by a formidable wall of mathematics. The approach in An Introduction to the Bootstrap avoids that wall. It arms scientists and engineers, as well as statisticians, with the computational techniques they need to analyze and understand complicated data sets.