False Omission Rate
Overview
The False Omission Rate (FOR) is a metric used to assess the proportion of false negative predictions among all negative predictions made by a classification model. It quantifies the rate of incorrect negative predictions relative to the total number of negative predictions.
Formula
FOR = P(actual = + | prediction = -) = FN/(TN+FN)
Where:
- FN (False Negatives): The number of positive instances incorrectly predicted as negative by the model.
- TN (True Negatives): The number of negative instances correctly predicted by the model.
Usage
Manually
# Usage example
for_priv, for_unpriv = false_omission_rate(df, protected_attribute, privileged_group, labels, positive_label, y_true)
print("False Omission Rate for privileged group:", for_priv)
print("False Omission Rate for unprivileged group:", for_unpriv)
Using Fairness Object
for_priv,for_unpriv = (fo.compute(false_omission_rate))
Results
False Omission Rate for privileged group: 0.16666666666388888
False Omission Rate for unprivileged group: 0.799999999984
These results are obtained by using the input data given in the Create Example Data page under Getting Started
Interpretation
A lower FOR value indicates that the model has a lower rate of false negative predictions among all negative predictions, which is desirable. A FOR of 0.0 indicates that there are no false negative predictions made by the model.
Updated 9 months ago