Precision vs Recall: Predicted-Positive Accuracy vs Actual-Positive Coverage

Mon, 03 Aug 2026 03:36:08 +0900

Overview

Precision and recall are two classification metrics computed from the same confusion matrix but answering different questions about a model’s positive predictions. Precision asks how many predicted positives were correct, while recall asks how many actual positives were found. Optimizing one in isolation almost always trades off against the other.

Comparison Diagram

Comparison Table

Aspect	Precision	Recall
Question answered	Of items predicted positive, how many actually are positive?	Of items that are actually positive, how many did the model find?
Formula	TP / (TP + FP)	TP / (TP + FN)
Denominator basis	Total predicted positive (TP + FP)	Total actual positive (TP + FN)
Error type penalized	False positives (false alarms)	False negatives (missed detections)
Increases when	Model makes fewer incorrect positive calls	Model catches more of the true positive cases
Threshold trade-off	Raising the decision threshold typically raises precision	Lowering the decision threshold typically raises recall
Failure mode at extreme	High precision, low recall: model is overly conservative and misses real cases	High recall, low precision: model is overly liberal and floods results with false alarms

Key Differences

Precision’s denominator is predicted positives; recall’s denominator is actual positives, so they measure against different totals
Precision is hurt by false positives; recall is hurt by false negatives
Adjusting the classification threshold pushes precision and recall in opposite directions
Neither metric alone summarizes model quality, which is why the F1 score combines them
A model with 100% recall can trivially predict everyone positive, and a model with 100% precision can trivially predict almost no one positive

When to Use Each

Precision

Model-Evaluation on IT Comparison

Precision vs Recall: Predicted-Positive Accuracy vs Actual-Positive Coverage

Overview

Comparison Diagram

Comparison Table

Key Differences

When to Use Each