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

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a pipeline.Pipeline. Otherwise it has no effect.

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