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Precision-recall curve

Next selectROC curves
Next selectComparison of precision-recall curves


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).

If MedCalc's comparison of precision-recall curves, the precision-recall curves of two dependent variables are constructed. "Dependent variables" means that the data of the two variables are derived from the same cases and are therefore paired.

MedCalc generates the precision-recall curves from the raw data (not from a sensitivity-PPV table), and calculates the difference between the areas under the two curves, together with the 95% BCa bootstrap confidence interval for this difference.

How to enter data for a precision-recall curve

In order to create the precision-recall curves you should have the two measurements of interest (= the parameters 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 measurements of interest.

In the spreadsheet, create a column Classification and two columns for the variables of interest, e.g. Param1 and Param2. 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 Param1 and Param2 columns, enter the measurements of interest for each case on the same row (this can be measurements, grades, etc. - if the data are categorical, code them with numerical values).

Data for comparison of precision-recall curves

Required input

Dialog box for comparison of precision-recall curves


Results for comparison of precision-recall curves

First MedCalc reports the following statistics for each variable:

See also a note on Criterion values.

Next, MedCalc reports


Comparison of precision-recall curves graph

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.


See also

External links