libuplift.meta.s_learner
========================

.. py:module:: libuplift.meta.s_learner

.. autoapi-nested-parse::

   The S-learner meta model.

   Simply add the treatment variable to a classifier/regressor.

   ..
       !! processed by numpydoc !!


Classes
-------

.. autoapisummary::

   libuplift.meta.s_learner.SLearnerUpliftRegressor
   libuplift.meta.s_learner.SLearnerUpliftClassifier


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

.. py:class:: SLearnerUpliftRegressor(base_estimator=LinearRegression(), treatment_encoding='one_hot')

   Bases: :py:obj:`libuplift.base.UpliftRegressorMixin`, :py:obj:`_SLearnerBase`


   
   Mixin class for all uplift regression estimators in
   libuplift.
















   ..
       !! processed by numpydoc !!

   .. py:method:: predict(X)


.. py:class:: SLearnerUpliftClassifier(base_estimator=LogisticRegression(), treatment_encoding='one_hot')

   Bases: :py:obj:`libuplift.base.UpliftClassifierMixin`, :py:obj:`_SLearnerBase`


   
   Mixin class for all uplift classification estimators in
   libuplift.
















   ..
       !! processed by numpydoc !!

   .. py:method:: predict(X)