libuplift.meta.response
=======================

.. py:module:: libuplift.meta.response

.. autoapi-nested-parse::

   'Fake' uplift models based on response classifiers.

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Classes
-------

.. autoapisummary::

   libuplift.meta.response.TreatmentUpliftClassifier
   libuplift.meta.response.ResponseUpliftClassifier
   libuplift.meta.response.ControlUpliftClassifier


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

.. py:class:: TreatmentUpliftClassifier(base_estimator=LogisticRegression(), reverse=False)

   Bases: :py:obj:`_ResponseModelBase`


   
   Predict uplift based on treatment classifiers.

   Ignore control.















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.. py:class:: ResponseUpliftClassifier(base_estimator=LogisticRegression(), reverse=False)

   Bases: :py:obj:`TreatmentUpliftClassifier`


   
   Predict uplift using a classifier built on full data.

   Ignore causal nature of the data.















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


.. py:class:: ControlUpliftClassifier(base_estimator=LogisticRegression(), reverse=True)

   Bases: :py:obj:`TreatmentUpliftClassifier`


   
   Predict uplift based on a control classifier.

   Ignore treatment data.  If reverse is True lower classification
   scores are assumed to correspond to higher uplift.















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