sklift.models.SoloModel

class sklift.models.models.SoloModel(estimator, method='dummy')[source]

aka Treatment Dummy approach, or Single model approach, or S-Learner.

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:

array-like, shape (n_samples, )

ctrl_preds_

Estimator predictions on samples when control.

Type:

array-like, shape (n_samples, )

Example:

# import approach
from sklift.models import SoloModel
# import any estimator adheres to scikit-learn 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. 78-86.

See also

Other approaches:

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 (array-like, shape (n_samples, n_features)) – Training vector, where n_samples is the number of samples and n_features is the number of features.

  • y (array-like, shape (n_samples,)) – Binary target vector relative to X.

  • treatment (array-like, 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

predict(X)[source]

Perform uplift on samples in X.

Parameters:

X (array-like, shape (n_samples, n_features)) – Training vector, where n_samples is the number of samples and n_features is the number of features.

Returns:

uplift

Return type:

array (shape (n_samples,))

set_fit_request(*, estimator_fit_params: bool | None | str = '$UNCHANGED$', treatment: bool | None | str = '$UNCHANGED$') → SoloModel

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:

  • estimator_fit_params (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for estimator_fit_params parameter in fit.

  • treatment (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for treatment parameter in fit.

Returns:

self – The updated object.

Return type:

object