sklift.models.ClassTransformation

class sklift.models.models.ClassTransformation(estimator)[source]

aka Class Variable Transformation or Revert Label approach.

Redefine target variable, which indicates that treatment make some impact on target or did target is negative without treatment: Z = Y * W + (1 - Y)(1 - W),

where Y - target vector, W - vector of binary communication flags.

Then, Uplift ~ 2 * (Z == 1) - 1

Returns only uplift predictions.

Read more in the User Guide.

Parameters:

estimator (estimator object implementing 'fit') – The object to use to fit the data.

Example:

# import approach
from sklift.models import ClassTransformation
# import any estimator adheres to scikit-learn conventions
from catboost import CatBoostClassifier


# define approach
ct = ClassTransformation(CatBoostClassifier(verbose=100, random_state=777))
# fit the model
ct = ct.fit(X_train, y_train, treat_train, estimator_fit_params={{'plot': True})
# predict uplift
uplift_ct = ct.predict(X_val)

References

Maciej Jaskowski and Szymon Jaroszewicz. Uplift modeling for clinical trial data. ICML Workshop on Clinical Data Analysis, 2012.

See also

Other approaches:

fit(X, y, treatment, estimator_fit_params=None)[source]

Fit the model according to the given training data.

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,)) – 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 = '$UNCHANGED$') → ClassTransformation

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