False Positive Rate
The FPR relates the number of false positives to the total number of negative elements. This estimates a likelihood of an element being predicted positive, if it is negative. This measure is based on the pairwise approach to calculate TP,TN,FP and FN.
\[ FPR = \frac{FP}{FP+TN} \]
In the binary classification background we have two classes that we want to distinguish:
positive and
negative.
In this scenario there are four possible outcomes:
- TP (True Positive): The object belongs to class positive and we classified it as positive,
- FP (False Positive ): The object belongs to class negative and we classified it as positive,
- TN (True Negative): The object belongs to class negative and we classified it as negative,
- FN (False Negative): The object belongs to class positive but we classified it as negative
| | Reality |
| | Positive | Negative |
Prediction | Positive | TP | FP |
Negative | FN | TN |