AI engineering glossary · Evaluation metrics
What is FN (false negative)?
False negative (also called FN, miss): A case that truly belongs to the class of interest but that the model labels as not belonging. It is a missed detection. In screening, safety and fraud settings a missed case is often costlier than a false alarm, and they drive down recall.
False negative: an example
A tuberculosis-screening model clears a patient's chest X-ray who actually has the disease. That missed case is a false negative.
Related terms
More Evaluation metrics terms
- False negative rate
- False positive
- False positive rate
- Fleiss' kappa
- G-mean
- Gini coefficient
- Hamming loss
- Hit rate
Where you learn it
False negative is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.