sklift.models.SoloModel¶

class
sklift.models.models.
SoloModel
(estimator, method='dummy')[source]¶ aka Treatment Dummy approach, or Single model approach, or SLearner.
Fit solo model on whole dataset with ‘treatment’ as an additional feature.
Each object from the test sample is scored twice: with the communication flag equal to 1 and equal to 0. Subtracting the probabilities for each observation, we get the uplift.
Return delta of predictions for each example.
Read more in the User Guide.
Parameters:  estimator (estimator object implementing 'fit') – The object to use to fit the data.
 method (string, ’dummy’ or ’treatment_interaction’, default='dummy') –
Specifies the approach:
'dummy'
: Single model;
'treatment_interaction'
: Single model including treatment interactions.

trmnt_preds_
¶ Estimator predictions on samples when treatment.
Type: arraylike, shape (n_samples, )

ctrl_preds_
¶ Estimator predictions on samples when control.
Type: arraylike, shape (n_samples, )
Example:
# import approach from sklift.models import SoloModel # import any estimator adheres to scikitlearn conventions from catboost import CatBoostClassifier sm = SoloModel(CatBoostClassifier(verbose=100, random_state=777)) # define approach sm = sm.fit(X_train, y_train, treat_train, estimator_fit_params={{'plot': True}) # fit the model uplift_sm = sm.predict(X_val) # predict uplift
References
Lo, Victor. (2002). The True Lift Model  A Novel Data Mining Approach to Response Modeling in Database Marketing. SIGKDD Explorations. 4. 7886.
See also
Other approaches:
ClassTransformation
: Class Variable Transformation approach.TwoModels
: Double classifier approach.
Other:
plot_uplift_preds()
: Plot histograms of treatment, control and uplift predictions.

fit
(X, y, treatment, estimator_fit_params=None)[source]¶ Fit the model according to the given training data.
For each test example calculate predictions on new set twice: by the first and second models. After that calculate uplift as a delta between these predictions.
Return delta of predictions for each example.
Parameters:  X (arraylike, shape (n_samples, n_features)) – Training vector, where n_samples is the number of samples and n_features is the number of features.
 y (arraylike, shape (n_samples,)) – Target vector relative to X.
 treatment (arraylike, shape (n_samples,)) – Binary treatment vector relative to X.
 estimator_fit_params (dict, optional) – Parameters to pass to the fit method of the estimator.
Returns: self
Return type: object