PR AUC Calculator
Calculate the area under the precision-recall curve for imbalanced classification.
About PR AUC Calculator
The Precision-Recall (PR) curve plots precision against recall across classification thresholds, and the area under it (PR-AUC, closely related to Average Precision) summarizes performance in a single number focused on the positive class. Unlike ROC-AUC, PR-AUC is highly sensitive to class imbalance in a useful way — because precision is influenced by how rare the positive class is, PR-AUC drops noticeably for models that struggle to find a rare positive class, making it a preferred metric for imbalanced problems like fraud or anomaly detection.
Formula
PR-AUC = sum((Precision_i + Precision_{i+1}) / 2 * (Recall_{i+1} - Recall_i))
- P_i
- Precision at point i
- R_i
- Recall at point i
Worked example
With the default values below, PR AUC Calculator returns PR-AUC of 0.7800. Change any input above and every figure here is recalculated in your browser.
Inputs used
- Recall, Precision Points
- 0,1; 0.2,0.95; 0.4,0.9; 0.6,0.8; 0.8,0.6; 1,0.3
Results
- PR-AUC
- 0.7800
- Number of Points
- 6
Step by step
- 1Sort points by recall ascending= 6 points
- 2PR-AUC: Σ trapezoid areas between consecutive points= 0.7800
Assumptions and limits
- The result is derived only from the single input listed above. Anything not among them is not part of the calculation.
- In particular, this calculator does not take inflation, tax, fees or charges or compounding frequency into account.
- Figures are rounded for display to 4 decimal places, so totals built from several rounded parts can differ slightly from the unrounded arithmetic.
How these calculators are built and tested is described on the Methodology page. If a figure looks wrong, please report it.
Frequently Asked Questions
Why prefer PR-AUC over ROC-AUC for imbalanced data?
ROC-AUC can look deceptively good on imbalanced datasets because the false positive rate denominator is dominated by the large negative class, while PR-AUC's precision term is directly affected by how many false positives occur relative to true positives, better reflecting real-world performance on the rare class.
What does a PR-AUC close to the positive class rate mean?
A PR-AUC roughly equal to the fraction of positive examples in the dataset indicates the model provides little discriminative value over a random classifier — a useful baseline to compare against.
Is PR-AUC the same as Average Precision?
They're closely related and often used interchangeably; Average Precision is typically computed as a weighted sum over precision values at each recall level achieved by actual predictions, which is numerically similar to trapezoidal integration of the PR curve.