AI engineering glossary · Evaluation metrics
What is FP (false positive)?
False positive (also called FP, Type I error in classification, false alarm): A case that does not belong to the class of interest but that the model wrongly labels as belonging. It is a false alarm. Each false positive costs something real, such as a blocked genuine payment or an unnecessary medical test, and they drive down precision.
False positive: an example
A bank's fraud model blocks a customer's genuine Rs 45,000 rent payment. The customer is annoyed and calls support.
Related terms
More Evaluation metrics terms
- False positive rate
- Fleiss' kappa
- G-mean
- Gini coefficient
- Hamming loss
- Hit rate
- Human evaluation
- Isotonic calibration
Where you learn it
False positive is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.