fairlearn.metrics.equal_opportunity_ratio#

fairlearn.metrics.equal_opportunity_ratio(y_true, y_pred, *, sensitive_features, method='between_groups', sample_weight=None)[source]#

Calculate the equal opportunity ratio.

The equal opportunity ratio is defined as the ratio between the smallest and the largest group-level true positive rate, \(P[h(X)=1 | A=a, Y=1]\), across all values \(a\) of the sensitive feature(s). The equal opportunity ratio of 1 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. See fairlearn.metrics.MetricFrame.ratio() for details.

  • sample_weight (array_like) – The sample weights

Return type:

float

Returns:

The equal opportunity ratio