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 arraylike) – Correct (true) target values.
uplift (1d arraylike) – Predicted uplift, as returned by a model.
treatment (1d arraylike) – 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
parameterAdd parameter
strategy
 Returns
Uplift score at first k observations of the total sample.
 Return type
float
See also
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.