# fairlearn.metrics.equalized_odds_difference#

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

Calculate the equalized odds difference.

The greater of two metrics: true_positive_rate_difference and false_positive_rate_difference. 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 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_true (array-like) – Ground truth (correct) labels.

• y_pred (array-like) – Predicted labels $$h(X)$$ returned by the classifier.

• sensitive_features – The sensitive features over which demographic parity should be assessed

• method (str) – How to compute the differences. See fairlearn.metrics.MetricFrame.difference() for details.

• sample_weight (array-like) – The sample weights

Returns

The equalized odds difference

Return type

float