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Also known as: FNR, Miss Rate
Proportion of actual positives incorrectly labeled as negative by a classifier.
DESCRIPTION
False Negative Rate measures how often a classifier misses actual positives. It is computed as FN divided by (FN + TP) and is also called the miss rate. Low FNR is critical in safety-critical and medical diagnostics where missing positives is risky. FNR trades off with Sensitivity (recall) as thresholds shift. Monitoring FNR helps balance risk between missed detections and false alarms.
ALTERNATIVE DEFINITIONS
Google Machine Learning Glossary
false negative rate (FNR): The fraction of positive examples that are incorrectly classified as negative.
Compact formulation highlighting fraction of positives missed.
Wikipedia
The false negative rate (FNR), also known as the miss rate, is the proportion of positives that are incorrectly identified as such, that is, the percentage of sick people who are incorrectly identified as healthy.
Connects FNR to medical example and miss rate terminology.
RELATED TERMS
Specificity Precision ROC Curve Confusion Matrix False Positive Rate Type II Error Balanced Accuracy Threshold
COMPETING TERMS
KNOWLEDGE AREAS
Data Science AI Mathematics Testing & Validation
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer Data Analyst
EXTERNAL SOURCES
- Wikipedia - Sensitivity and specificity
- scikit-learn - Overview of classification metrics including recall/FNR relations.
- Microsoft Learn - Model evaluation guide covering classification errors.
TOP QUESTIONS
What is False Negative Rate and when is it most critical?
False Negative Rate is the share of positives predicted as negative. It is most critical when missing a positive is costly, such as cancer screening or fault detection.
Category: Relevancy
How do you compute False Negative Rate?
FNR = FN / (FN + TP). It quantifies the proportion of missed positives among all actual positives.
Category: Requirements
How does False Negative Rate relate to Sensitivity (Recall)?
FNR is 1 − Sensitivity. Reducing FNR increases recall, capturing more positives.
Category: Relationship
What threshold strategy reduces False Negative Rate?
Lowering the decision threshold typically reduces FNR, at the risk of increasing FPR. Select thresholds based on acceptable risk trade-offs.
Category: Management
What is a common misunderstanding about False Negative Rate?
People often confuse FNR with overall error rate. FNR specifically concerns missed positives, not all errors.
Category: Misunderstanding
How can class imbalance affect False Negative Rate?
With rare positives, naive models can have deceptively low FNR if thresholds are tuned poorly. Use stratified evaluation and proper metrics.
Category: Risk
How does False Negative Rate show up on the ROC curve?
ROC displays TPR (1 − FNR) on the y-axis versus FPR on the x-axis. Higher curves imply lower FNR for a given FPR.
Category: Example
Which metric competes conceptually with False Negative Rate?
Sensitivity (Recall) is the direct counterpart, measuring the positives correctly identified.
Category: Alternatives
How to monitor False Negative Rate in production?
Track confusion matrix over time with labeled feedback or proxy labels. Use alerting when FNR exceeds policy thresholds.
Category: Observability