Precision-Recall Curve Generator

What does Precision–Recall Curve Generator show?

Precision–Recall Curve Generator shows the trade-off between positive predictive value and sensitivity as a classification threshold changes. It is especially useful when positive cases are rare and false positives matter.

Data requirements for Precision–Recall Curve Generator

• Provide one observed binary label and one continuous score or predicted probability per case.
• Add an optional model, fold, cohort, or subgroup column for comparisons.
• Keep the positive-class definition consistent across all series.
• Use predictions from held-out or properly cross-validated data rather than training predictions.

Research examples for Precision–Recall Curve Generator

• Precision-Recall Curve for Imbalanced Classification
• PR AUC Curve for Disease Screening
• Multiclass Precision-Recall Curve
• ROC vs Precision-Recall Model Comparison
• Object Detection Precision-Recall Curve
• Precision-Recall Curve with Average Precision

Why use a precision–recall curve?

• Evaluate classifiers when the positive class is uncommon.
• See the cost of gaining recall in additional false-positive predictions.
• Compare models at thresholds relevant to the application.
• Report average precision or area measures alongside the full curve rather than alone.

How to use Precision-Recall Curve Generator

Choose an example or upload one CSV, TSV, TXT, XLSX, or XLS file, check the detected columns, generate the chart, and continue editing it in SciVis. For Excel files, the first worksheet is imported.

What a clear precision–recall curve should include

• Recall on the x-axis and precision on the y-axis.
• A label stating which outcome is the positive class.
• A no-skill baseline tied to positive-class prevalence.
• Separate curves and uncertainty estimates for models or validation folds when available.

Precision–recall curve or ROC curve?

Use precision–recall when positive cases are rare or false positives directly affect usefulness. ROC curves summarize sensitivity and specificity and can appear optimistic under severe class imbalance. Reporting both is often appropriate.