# sklift.metrics.uplift_at_k¶

sklift.metrics.metrics.uplift_at_k(y_true, uplift, treatment, strategy, k=0.3)[source]

Compute uplift at first k observations by uplift of the total sample.

Parameters: y_true (1d array-like) – Correct (true) target values. uplift (1d array-like) – Predicted uplift, as returned by a model. treatment (1d array-like) – Treatment labels. k (float or int) – If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the computation of uplift. If int, represents the absolute number of samples. strategy (string, ['overall', 'by_group']) – Determines the calculating strategy. 'overall': The first step is taking the first k observations of all test data ordered by uplift prediction (overall both groups - control and treatment) and conversions in treatment and control groups calculated only on them. Then the difference between these conversions is calculated. 'by_group': Separately calculates conversions in top k observations in each group (control and treatment) sorted by uplift predictions. Then the difference between these conversions is calculated
Changed in version 0.1.0:
• Add supporting absolute values for k parameter

• Add parameter strategy

Returns: Uplift score at first k observations of the total sample. float

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