fairlearn.metrics.equal_opportunity_difference#
- fairlearn.metrics.equal_opportunity_difference(y_true, y_pred, *, sensitive_features, method='between_groups', sample_weight=None)[source]#
Calculate the equal opportunity difference.
The equal opportunity difference is defined as the difference between the largest and the smallest group-level true positive rates, \(P[h(X)=1 | A=a, Y=1]\), across all values \(a\) of the sensitive feature(s). The equal opportunity difference of 0 means that all groups have the same true positive rate.
Read more in the User Guide.
Added in version 0.11.0.
- Parameters:
y_true (array_like) – Ground truth (correct) labels.
y_pred (array_like) – Predicted labels \(h(X)\) returned by the classifier.
sensitive_features (array_like) – The sensitive features over which equal opportunity should be assessed
method (
Literal['between_groups','to_overall']) – How to compute the differences. Seefairlearn.metrics.MetricFrame.difference()for details.sample_weight (array_like) – The sample weights
- Return type:
- Returns:
The equal opportunity difference