sklift.metrics.qini_auc_score

sklift.metrics.metrics.qini_auc_score(y_true, uplift, treatment, negative_effect=True)[source]

Compute normalized Area Under the Qini curve (aka Qini coefficient) from prediction scores.

By computing the area under the Qini curve, the curve information is summarized in one number. For binary outcomes the ratio of the actual uplift gains curve above the diagonal to that of the optimum Qini curve.

Parameters

  • y_true (1d array-like) – Correct (true) binary target values.

  • uplift (1d array-like) – Predicted uplift, as returned by a model.

  • treatment (1d array-like) – Treatment labels.

  • negative_effect (bool) –

    If True, optimum Qini Curve contains the negative effects (negative uplift because of campaign). Otherwise, optimum Qini Curve will not contain the negative effects.

    New in version 0.2.0.

Returns

Qini coefficient.

Return type

float

See also

qini_curve(): Compute Qini curve.

perfect_qini_curve(): Compute the perfect (optimum) Qini curve.

plot_qini_curves(): Plot Qini curves from predictions..

uplift_auc_score(): Compute normalized Area Under the Uplift curve from prediction scores.

References

Nicholas J Radcliffe. (2007). Using control groups to target on predicted lift: Building and assessing uplift model. Direct Marketing Analytics Journal, (3):14–21, 2007.