fairlearn.metrics.equal_opportunity_difference

fairlearn.metrics.equal_opportunity_difference(y_true, y_pred, *, sensitive_features, method='between_groups', sample_weight=None)

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, \(E[h(X) | A=a]\), 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.

Return type:

float

Parameters:

y_truearray-like

Ground truth (correct) labels.

y_predarray-like

Predicted labels \(h(X)\) returned by the classifier.

sensitive_featuresarray-like

The sensitive features over which equal opportunity should be assessed

methodstring {‘between_groups’, ‘to_overall’}, default between_groups

How to compute the differences. See fairlearn.metrics.MetricFrame.difference() for details.

sample_weightarray-like

The sample weights

Returns:

float

The equal opportunity difference