Intermediate · Evaluation
False positive rate (FPR)
Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.
TL;DR. The false positive rate measures the proportion of actual negative instances that were incorrectly predicted as positive.
Technical Definition
The false positive rate measures the proportion of actual negative instances that were incorrectly predicted as positive.
How it works
The false positive rate (FPR), also known as the fall-out, quantifies the errors made by a model in identifying negative cases. It is calculated as the number of false positives divided by the sum of false positives and true negatives. The FPR is a crucial metric, particularly in the context of Receiver Operating Characteristic (ROC) curves.
Related Concepts
- ROC Curve — A plot of true-positive rate against false-positive rate across all classification thresholds.
- 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.