Beginner · Evaluation
False positive (FP)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. A false positive occurs when a model incorrectly predicts the positive class for a data point that actually belongs to the negative class.
Technical Definition
A false positive occurs when a model incorrectly predicts the positive class for a data point that actually belongs to the negative class.
How it works
In binary classification, a false positive (FP) is an error where a model mistakenly classifies an instance of the negative class as the positive class. For instance, an email spam filter might incorrectly flag a legitimate email as spam.
Related Concepts
- Confusion Matrix — A table showing true vs predicted labels for every class.
- False negative (FN) — A false negative occurs when a model incorrectly predicts the negative class for a data point that actually belongs to the positive class.