A side-by-side comparison of Accuracy and Recall. Understand how overall correctness differs from the ability to find actual positive cases, especially when missed positives are costly.
Quick Verdict: Use Accuracy when overall correctness is meaningful; use Recall when failing to identify actual positives creates material risk.
Accuracy measures classification metric equal to the proportion of predictions that are correct among all predictions made.
Context: Most relevant when all classes and error types have similar importance and the dataset is not severely imbalanced.
Recall measures evaluation metric that measures the proportion of actual positive or relevant items that a model successfully identifies.
Context: Most relevant for detection, screening, and safety-sensitive tasks where missing a positive case is costly.
| Aspect | Accuracy | Recall |
|---|---|---|
| What it measures | Accuracy measures correct predictions among all predictions. | Recall measures identified positives among all actual positives. |
| Best use case | Useful when classes are balanced and errors have similar cost. | Useful when missed positives are more costly than extra false alarms. |
| Failure mode | Can look high even when a model misses many rare but important positives. | Can look high even when many predicted positives are false positives. |
| Threshold sensitivity | Changing a classification threshold can change accuracy by shifting the balance of correct and incorrect predictions. | Changing a threshold often directly changes recall by making the model more or less willing to identify positives. |
| Common mistake | Reporting accuracy alone for imbalanced or high-risk classification tasks. | Maximizing recall without documenting the false-positive trade-off or operational burden. |
In practice, accuracy is the metric most likely to look reassuring while hiding the problem; recall forces teams to ask what the model fails to catch.
Treating high accuracy as proof that a classifier is safe for rare-event detection.
Reporting recall without precision, false positives, or threshold assumptions.
Choosing metrics before defining which error type is more harmful.
Use Accuracy when you need a simple measure of total correct classifications and the class distribution makes that number meaningful. It should not be the only metric when positive cases are rare or error costs differ.
Use Recall when the evaluation question is how many actual positive or relevant items the model successfully finds. It is especially important in screening, safety, fraud, compliance, and other settings where missed positives are costly.
Metric selection affects validation reports, assurance evidence, and performance claims under AI governance programs. NIST AI RMF and ISO 42001-style controls should document why a chosen metric matches the risk of the use case.
Yes. If positive cases are rare, a model can correctly classify many negative cases and still miss many actual positives.
No. Recall is better when missed positives are especially important, while accuracy can be useful when overall correctness is meaningful and class balance is reasonable.
Often yes. Reports should explain why each metric is relevant, what error trade-offs exist, and how the metric supports the risk assessment.
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