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Also known as: FN
False negatives are duplicate pairs incorrectly classified as non-matches, reducing recall and leaving entities fragmented.
DESCRIPTION
A false negative occurs when a model misses a positive instance, predicting it as negative. In medical screening, this means failing to detect an existing condition, impacting safety. False negatives relate to sensitivity (recall) and are often reduced by lowering decision thresholds. Managing FN trade-offs requires context-specific cost analysis versus false positives. Monitoring class imbalance and drift is essential to control FN rates in production.
ALTERNATIVE DEFINITIONS
Wikipedia
A false negative is an error in which a test result incorrectly indicates that a condition does not hold when in fact it does.
Connects false negatives to statistical testing and Type II error.
Google Developers ML Glossary
An example in which the model incorrectly predicts the negative class.
Frames false negatives in ML classification context.
NIST Computer Security Resource Center
A test result that incorrectly indicates that a condition is absent when it is actually present.
Security-focused phrasing emphasizing detection failure.
RELATED TERMS
Type II Error Precision Recall Specificity Confusion Matrix F1 Score Threshold Thresholding Evaluation Metrics Matching Identity Resolution Probabilistic Algorithms Blocking
COMPETING TERMS
KNOWLEDGE AREAS
Data Science AI Testing & Validation Data Quality Data Management
TARGET AUDIENCE
Data Scientist AI Engineer MLOps Engineer Data Analyst Data Steward Data Engineer BI Specialist
EXTERNAL SOURCES
- Wikipedia - Type I and type II errors
- Google Developers - Explains trade-offs and thresholds
- scikit-learn - Confusion matrix and metrics
- Microsoft Learn - Model metrics including FN
TOP QUESTIONS
Why do False Negatives matter?
They represent missed detections, which can be costly or dangerous in domains like healthcare and fraud. Reducing false negatives often improves recall but may increase false positives.
Category: Relevancy
How do False Negatives relate to recall?
Recall (sensitivity) measures the proportion of actual positives correctly identified, so higher false negatives mean lower recall. Adjusting thresholds or class weights can improve recall.
Category: Example
What causes high False Negative rates?
Common causes include high decision thresholds, class imbalance, insufficient features, and over-regularized models. Data drift can also increase false negatives over time.
Category: Mistakes
How can I reduce False Negatives?
Lower the classification threshold, optimize for recall, use cost-sensitive learning, resample classes, and enrich features. Monitor post-deployment metrics for drift and recalibrate regularly.
Category: Management
What is the trade-off between False Negatives and False Positives?
Reducing false negatives often increases false positives. The optimal balance depends on domain-specific costs and risks, sometimes formalized through utility or cost matrices.
Category: Opinion
How are False Negatives monitored in production?
Track recall, confusion matrix components, and alert on drift. Where labels arrive later, use delayed ground truth linking and periodic backtesting to estimate false negative rates.
Category: Testing
What metrics complement False Negatives?
Sensitivity/recall, specificity, precision, F1 score, ROC-AUC, and PR-AUC provide broader context. Calibration curves help understand threshold effects on false negatives.
Category: Overview
Are False Negatives always worse than False Positives?
No. It depends on the application. In cancer screening, false negatives are critical; in spam filtering, false positives may be more disruptive. Context determines priority.
Category: Opinion
How does class imbalance affect False Negatives?
Imbalanced datasets can bias models toward the majority class, increasing false negatives for the minority positive class. Techniques like resampling and focal loss mitigate this.
Category: Risk