Intermediate · Evaluation
False negative rate
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. The false negative rate measures the proportion of actual positive instances that were incorrectly predicted as negative.
Technical Definition
The false negative rate measures the proportion of actual positive instances that were incorrectly predicted as negative.
How it works
The false negative rate, also known as sensitivity or recall, quantifies the errors made by a model in identifying positive cases. It is calculated as the number of false negatives divided by the sum of false negatives and true positives. A low false negative rate is desirable, indicating that the model correctly identifies most positive instances.
Related Concepts
- Recall — Of all the actually positive items, the fraction the model successfully found.
- 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.