Read Precision–Recall Results in Their Population Context

Define the positive class and prediction task

Precision is the proportion of predicted positives that are true positives. Recall is the proportion of actual positives identified by the rule. In a binary clinical classification task, these correspond to positive predictive value and sensitivity when the labels and outcome ascertainment are appropriate.

The scikit-learn precision–recall reference describes the quantities and threshold outputs. Confirm which label is positive before comparing curves; reversing labels changes the question. A probability score and a hard class label provide different amounts of information for this analysis.

Preserve the evaluation population

Record the event frequency, sampling scheme and any case-control enrichment. Precision depends on the prevalence represented by the evaluation data, so a curve from an artificially balanced sample cannot be interpreted as population positive predictive value without an appropriate adjustment.

Keep threshold-specific counts alongside the curve. Identify operating points relevant to the intended use rather than choosing a visually attractive point after examining the test outcomes. When summarizing a curve, state whether the reported quantity is average precision or a particular numerical area calculation; different conventions need not agree.

Calculate a hypothetical operating point

Suppose 100 fictional people include 20 with the outcome. A rule flags 30 people, of whom 15 have the outcome. Precision is 15/30 = 50%, and recall is 15/20 = 75%. The rule also produces 15 false positives and misses five outcome-positive people.

If the same sensitivity and specificity were applied in a population with a different event frequency, precision would generally change. Therefore, 50% should not be carried into another setting as an intrinsic property of the classifier. The count table makes both the benefit and the workload implications easier to inspect.

Compare models without hiding uncertainty

Use the same evaluation people, outcome definitions and weighting scheme when comparing models. Account for paired predictions and any clustering in uncertainty estimates. Small numbers of positive outcomes can make portions of a precision–recall curve unstable even when the total evaluation sample looks large.

Report the event count, relevant thresholds and intervals where appropriate. Explain the consequences of false positives and false negatives separately from the curve summary. A model with a higher average precision is not automatically preferable at every operating threshold or for every clinical workflow. The final interpretation should connect the observed performance to a defined population and decision rather than to an isolated score.

Source verification: 6 November 2026. Examples are hypothetical.