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