False Positive Rate
Overview
Calculates FP / (FP + TN)
for privileged and unprivileged groups. This is the rate of false positives seen out of all the negative events.
Formula
FPR = FP / (FP + TN)
Where:
-
TN (True Negatives): The number of negative instances correctly predicted by the model
-
FP (False Positives): The number of positive instances incorrectly predicted as positive by the model
Usage
Manually
fpr_priv, fpr_unpriv = false_positive_rate(df, protected_attribute, privileged_group, labels, positive_label, y_true)
print("False Positive Rate for privileged group:", fpr_priv)
print("False Positive Rate for unprivileged group:", fpr_unpriv)
Using Fairness Object
fpr_priv, fpr_unpriv = (fo.compute(false_positive_rate))
Results
False Positive Rate for privileged group: 0.3749999999953125
False Positive Rate for unprivileged group: 0.74999999998125
These results are obtained by using the input data given in the Create Example Data page under Getting Started
Interpretation
A higher FPR indicates a greater likelihood to falsely predict a positive result.
Updated 8 months ago