aif360.sklearn.metrics.disparate_impact_ratio¶
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aif360.sklearn.metrics.disparate_impact_ratio(*y, prot_attr=None, priv_group=1, pos_label=1, sample_weight=None)[source]¶ Ratio of selection rates.
\[\frac{Pr(\hat{Y} = \text{pos_label} | D = \text{unprivileged})} {Pr(\hat{Y} = \text{pos_label} | D = \text{privileged})}\]Note
If only y_true is provided, this will return the ratio of base rates (disparate impact of the original dataset). If both y_true and y_pred are provided, only y_pred is used.
Parameters: - y_true (pandas.Series) – Ground truth (correct) target values. If y_pred is provided, this is ignored.
- y_pred (array-like, optional) – Estimated targets as returned by a classifier.
- prot_attr (array-like, keyword-only) – Protected attribute(s). If
None, all protected attributes in y_true are used. - priv_group (scalar, optional) – The label of the privileged group.
- pos_label (scalar, optional) – The label of the positive class.
- sample_weight (array-like, optional) – Sample weights.
Returns: float – Disparate impact.
See also