AI engineering glossary · Evaluation metrics
What is FPR (false positive rate)?
False positive rate (also called FPR, fall-out): The share of real negative cases that the model wrongly flags as positive: FP / (FP + TN). It equals one minus specificity and forms the horizontal axis of the ROC curve. It tells you how often the system raises false alarms among cases that were actually fine.
False positive rate: an example
10 genuine transactions are blocked out of 980 genuine ones, so FPR = 10 / 980, about 0.0102 or 1%.
Related terms
More Evaluation metrics terms
- Fleiss' kappa
- G-mean
- Gini coefficient
- Hamming loss
- Hit rate
- Human evaluation
- Isotonic calibration
- Jaccard index
Where you learn it
False positive 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.