libuplift.classifiers.memoized
==============================

.. py:module:: libuplift.classifiers.memoized

.. autoapi-nested-parse::

   A memoized classifier class.

   Used to avoid recomputing the same classifier twice e.g. when both
   T-learner and Response models are computed.

   ..
       !! processed by numpydoc !!


Classes
-------

.. autoapisummary::

   libuplift.classifiers.memoized.MemoizedClassifier


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

.. py:class:: MemoizedClassifier(estimator, memory=None)

   Bases: :py:obj:`sklearn.base.BaseEstimator`


   
   Creates a memoized version of an estimator.

   Subsequent calls to fit with the same arguments will reuse a
   prefitted model.

   memory is either a path or a joblib.Memory object.  If None a
   default path is used: "libuplift_cache" in systems default
   temporary directory.

   :Parameters:

       **estimator** : a scikit-klearn
           EstimatClassifier to wrap in a regessor interface.

       **memory** : a joblib.Memory object, default=None
           ..














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   .. py:method:: fit(*args, **kwargs)


   .. py:method:: __getattr__(name)