Precision-Recall Curve
A plot evaluating classifier performance by trading off precision against recall across thresholds.
A precision-recall curve is a diagnostic tool for evaluating binary classification models. It plots precision on the y-axis against recall on the x-axis across all possible decision thresholds. Precision measures the fraction of positive predictions that are actually correct. Recall, also called sensitivity, measures the fraction of true positives that the model identifies. As the decision threshold is lowered, a model typically captures more true positives, raising recall, but also accepts more false positives, which lowers precision. The curve traces this trade-off across the full threshold range.
The curve is useful when class distributions are heavily skewed. In such settings, the ROC curve can give an overly optimistic picture because it accounts for true negatives, which are abundant in imbalanced datasets and inflate apparent performance. The precision-recall curve avoids this by focusing only on the positive class, making it the preferred evaluation tool in domains like fraud detection, rare disease diagnosis, and information retrieval, where the minority class is the main concern. A model with a curve that hugs the top-right corner of the plot, maintaining high precision even at high recall, is considered strong.
A single scalar summary of the curve is often computed as the area under the precision-recall curve (AUPRC), sometimes called average precision. This metric aggregates performance across all thresholds into one number, enabling straightforward comparison between models. AUPRC is less affected by class imbalance than accuracy and rewards models that rank true positives highly. Practitioners also use the curve interactively to select an operating threshold that satisfies application-specific constraints. A medical screening tool, for example, might prioritize recall to minimize missed diagnoses, accepting lower precision as a consequence.
The concept comes from information retrieval research in the 1970s and 1980s, where precision and recall were standard metrics for evaluating document search systems. It moved into machine learning evaluation practice during the mid-2000s as large, imbalanced datasets became common in spam filtering, bioinformatics, and computer vision, making it a standard benchmark tool in modern ML workflows.
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