libuplift.meta.nested
=====================

.. py:module:: libuplift.meta.nested

.. autoapi-nested-parse::

   Nested models where control outcome predictions are used in an
   uplift model.

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Classes
-------

.. autoapisummary::

   libuplift.meta.nested.NestedMeanUpliftRegressor
   libuplift.meta.nested.DDRUpliftClassifier


Module Contents
---------------

.. py:class:: NestedMeanUpliftRegressor(base_estimator=LinearRegression())

   Bases: :py:obj:`libuplift.base.UpliftRegressorMixin`, :py:obj:`libuplift.meta.base.UpliftMetaModelBase`


   
   Nested regression model.

   First builds a model on controls, then subtracts its training
   predictions from target.  An uplift model is then build on the new
   target.

   Only available for regression models.















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   .. py:method:: predict(X)


.. py:class:: DDRUpliftClassifier(base_estimator=LogisticRegression(), feature_prediction_method='predict_proba', direction='C->T')

   Bases: :py:obj:`libuplift.base.UpliftClassifierMixin`, :py:obj:`libuplift.meta.base.UpliftMetaModelBase`


   
   Dependent Data Representation metamodel.  It is a double model
   where control predictions are added as a variable in the treatment
   model.

   The model was proposed in A. Betlei, E. Diemert, and M.-R. Amini
   Uplift Prediction with Dependent Feature Representation in
   Imbalanced Treatment and Control Conditions, ICONIP, 2018.

   direction : string, default="C->T" "C->T" means control
       predictions are used as an additional predictor for the
       treatment model, "T->C" means the reverse: predictions of all
       treatment models are used (jointly) as predictors for the
       control model.















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   .. py:method:: predict(X)