libuplift.meta.dr_learner
=========================

.. py:module:: libuplift.meta.dr_learner

.. autoapi-nested-parse::

   The DR-learner model from Kennedy 2004.

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Classes
-------

.. autoapisummary::

   libuplift.meta.dr_learner.DRLearnerBase
   libuplift.meta.dr_learner.DRLearnerUpliftRegressor
   libuplift.meta.dr_learner.DRLearnerUpliftClassifier


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

.. py:class:: DRLearnerBase(base_estimator=LinearRegression(), mean_estimator=None, cv=2)

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


   
   Base class for uplift meta estimators.

   Checks input consistency, builds classifiers on subsets of data.

   Derived classess need to overwride the _get_model_names_list and
   _iter_training_subsets methods.  The predict method needs to be
   implemented as well.















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   .. py:method:: predict(X)


   .. py:method:: fit(X, y, trt, n_trt=None, sample_weight=None, *, y_stratify=None)


.. py:class:: DRLearnerUpliftRegressor(base_estimator=LinearRegression(), mean_estimator=None, cv=2)

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


   
   Mixin class for all uplift regression estimators in
   libuplift.
















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.. py:class:: DRLearnerUpliftClassifier(base_estimator=LinearRegression(), mean_estimator=None, cv=2)

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


   
   The classifier works by treating the class variable as 0/1 real
   target and using a DRLearnerUpliftRegressor.

   The main difference is that stratification takes the target
   variable into account.















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   .. py:method:: fit(X, y, trt, n_trt=None, sample_weight=None, *, y_stratify=None)


   .. py:method:: predict(X)