AI engineering glossary · Evaluation metrics
What is FNR (false negative rate)?
False negative rate (also called FNR, miss rate): The share of real positive cases that the model misses: FN / (FN + TP). It equals one minus recall. It is used when a regulator or clinician cares about the proportion of true cases that slip through undetected.
False negative rate: an example
The fraud model misses 5 of 20 frauds, so FNR = 5 / 20 = 0.25.
Related terms
- Recall
- False negative
- False positive rate
- Detection error tradeoff curve
More Evaluation metrics terms
- False positive
- False positive rate
- Fleiss' kappa
- G-mean
- Gini coefficient
- Hamming loss
- Hit rate
- Human evaluation
Where you learn it
False negative rate is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.