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:
ClassTransformationReg
: Transformed Outcome approach.SoloModel
: Single model approach.TwoModels
: Double classifier approach.
- 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
(seesklearn.set_config()
). Please see User Guide on how the routing mechanism works.The options for each parameter are:
True
: metadata is requested, and passed tofit
if provided. The request is ignored if metadata is not provided.False
: metadata is not requested and the meta-estimator will not pass it tofit
.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 infit
.treatment (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for
treatment
parameter infit
.
- Returns:
self – The updated object.
- Return type:
object