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, \(P[h(X)=1 | A=a, Y=1]\), 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.

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

methodstr {‘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