fairlearn.metrics.equalized_odds_difference#

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

Calculate the equalized odds difference.

Depending on the agg parameter, this function computes either: :rtype: float

  • The greater of two metrics: true_positive_rate_difference and

false_positive_rate_difference, if agg=’worst_case’. The former is the difference between the largest and smallest of \(P[h(X)=1 | A=a, Y=1]\), across all values \(a\) of the sensitive feature(s). The latter is defined similarly, but for \(P[h(X)=1 | A=a, Y=0]\). - The mean of these two metrics, if agg=’mean’.

The equalized odds difference of 0 means that all groups have the same true positive, true negative, false positive, and false negative rates.

Read more in the User Guide.

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 equalized odds 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

aggstring {‘worst_case’, ‘mean’}, default worst_case

The aggregation method. One of “worst_case” or “mean”. If “worst_case”, the greater one of the false positive rate difference and true positive rate difference is returned. If “mean”, the mean of the differences is returned.

Returns:
float

The equalized odds difference