Precision-Recall Curve
Chart overview
Precision-Recall curves display the trade-off between positive predictive value and sensitivity at all classification thresholds, which is more informative than ROC curves when positive cases are rare.
Key points
- Clinical informaticists and bioinformaticians use PR curves to evaluate disease detection algorithms, rare variant classifiers, and rare event prediction models where class imbalance makes ROC AUC overly optimistic.
- The area under the PR curve and F1 iso-lines help identify operating points that balance false positives and false negatives.
Example Visualization
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"Create a precision-recall curve from my data. Plot recall on the x-axis and precision on the y-axis. Shade the area under the curve and annotate average precision in the legend. Draw F1 iso-curves as dashed lines across the plot. Add a horizontal dashed line at the baseline precision (prevalence) as the no-skill reference. Use journal formatting with Arial font and no top or right spines."
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Common Use Cases
- 1Evaluating rare disease detection models where positive class prevalence is below 5%
- 2Comparing clinical NLP algorithms for extracting rare diagnoses from electronic health records
- 3Assessing genomic variant pathogenicity prediction models on imbalanced variant datasets
- 4Selecting operating thresholds for screening tools where false positives carry high burden
Pro Tips
Always include the no-skill baseline (horizontal line at prevalence) to contextualize model performance
Overlay F1 iso-curves to help readers identify the threshold that maximizes the harmonic mean
Report area under the PR curve (average precision) rather than just AUC-ROC for imbalanced datasets
Use step-interpolation when plotting to accurately represent threshold-based behavior
Scientific Chart Selection Cheat Sheet
Not sure whether to use a Violin Plot, Box Plot, or Ridge Plot? Download our single-page reference mapping the most-used scientific chart types, exactly when to use them, and the core Matplotlib/Seaborn functions.