Up nextNext guide
Recall, Precision and Elusion Testing in Document Review
Technical
Fundamentals GUIDE
Precision and Recall
Precision and recall are two complementary metrics for evaluating classifiers, especially when classes are imbalanced.
Listen to this guideAI-narrated · ~2 min
On this page2 min read
Overview
Together they reveal what plain accuracy hides — how often a model's positive predictions are right, and how many real positives it actually catches.
Deep Dive
When a model flags items as positive, two questions matter. Precision asks: of everything we flagged, how much was truly positive? It equals true positives divided by all predicted positives, penalizing false alarms. Recall (sensitivity) asks: of all the real positives out there, how many did we catch? It equals true positives divided by all actual positives, penalizing misses. These usually trade off: lowering the decision threshold catches more positives (higher recall) but flags more junk (lower precision), and vice versa. Which to prioritize depends on costs — a spam filter favors precision (don't trash real mail), while a cancer screen favors recall (don't miss a tumor). The F1 score, their harmonic mean, balances both in one number.
Real-World Implementation
Spam filters tune for high precision so legitimate emails are almost never wrongly sent to the spam folder.
Medical screening tests prioritize high recall to avoid missing patients who actually have the disease, accepting more false positives for follow-up.
Search and recommendation systems report precision@k (how many of the top k results are relevant) to measure ranking quality.
Fraud detection balances precision and recall via the F1 score, since both false alarms and missed fraud are costly.
The Future of Precision and Recall
As AI enters high-stakes domains — medical diagnosis, content moderation, fraud — teams increasingly report precision and recall (and their curves) rather than accuracy alone, and tune thresholds to match real-world costs and fairness constraints. Per-group precision/recall audits are becoming standard for detecting disparate error rates across demographics. Expect richer cost-sensitive metrics, calibrated probabilities, and tooling that lets stakeholders pick operating points interactively rather than accepting a default 0.5 threshold.
Putting it into practice
Why it matters
- Clearer decisions. It helps you separate clear technical claims from marketing language.
- Cost and budget. You can ask better implementation questions before spending money or time.
- Team and workflow. Teams with shared understanding make better product, policy, and learning decisions.
Watch out for
- Different teams may use the same term differently, so define scope early.
- Benchmarks can look strong while real-world performance is uneven.
- Ignoring data quality and evaluation plans often creates fragile outcomes.
Next steps
- Start with a plain-language definition of the outcome you need.
- Pick one success metric and one failure condition before testing.
- Run a small pilot with representative data, not a polished demo set.
- Document where Precision and Recall helps and where simpler methods are better.
Keep Exploring
Free newsletter
Keep up with AI in 3 minutes a day
One short email each weekday with the three AI stories that actually matter. Free forever, no ads.
One email each weekday. Unsubscribe in one click. We never sell or share your address.
Test yourself
Take the Precision and Recall quiz
Instant feedback on every answer, and a shareable certificate with a verifiable ID once you pass a course.
Support free AI education. AI Understanding is a 501(c)(3) nonprofit — no ads, no paywall, ever. Make a donation
Frequently asked questions
What is Precision and Recall?
Precision and recall are two complementary metrics for evaluating classifiers, especially when classes are imbalanced. Together they reveal what plain accuracy hides — how often a model's positive predictions are right, and how many real positives it actually catches.
Precision answers which question?
Precision = TP / (TP + FP) measures the correctness of the model's positive predictions — how trustworthy a positive flag is.
On a fixed evaluation set, lowering a positive-class decision threshold usually produces which tradeoff?
Lowering the threshold expands or preserves the predicted-positive set. It may catch additional true positives and additional false positives; changes depend on which items cross the threshold.
Why can precision and recall reveal a weakness hidden by high accuracy on a dataset dominated by negatives?
Accuracy can be dominated by correct negative predictions. Precision and recall examine positive flags and missed positives using TP, FP, and FN.
Six of the top ten search results are relevant. What is precision@10 for that result list?
Precision@10 measures the relevant fraction among the top ten results: 6/10 = 60%.
Keep learning
Related guides
More guides picked for this topic