libuplift.datasets.Information
==============================

.. py:module:: libuplift.datasets.Information

.. autoapi-nested-parse::

   The marketing campaign dataset from the CRAN Information package by
   Kim Larsen.

   See: https://cran.r-project.org/web/packages/Information/index.html
   for details.

   ..
       !! processed by numpydoc !!


Functions
---------

.. autoapisummary::

   libuplift.datasets.Information.fetch_Information


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

.. py:function:: fetch_Information(version='train', data_home=None, download_if_missing=True, random_state=None, shuffle=False, categ_as_strings=False, return_X_y=False, as_frame=False)

   
   Load the marketing campaign dataset from the CRAN Information
   package by Kim Larsen.

   See: https://cran.r-project.org/web/packages/Information/index.html

   Two datasets are available: "train" and "validation".  Use version
   argument to select.

   Download it if necessary.

   :Parameters:

       **data_home** : string, optional
           Specify another download and cache folder for the datasets. By default
           all scikit-learn data is stored in '~/scikit_learn_data' subfolders.

       **download_if_missing** : boolean, default=True
           If False, raise a IOError if the data is not locally available
           instead of trying to download the data from the source site.

       **random_state** : int, RandomState instance or None (default)
           Determines random number generation for dataset shuffling. Pass an int
           for reproducible output across multiple function calls.

       **shuffle** : bool, default=False
           Whether to shuffle dataset.

       **categ_as_strings** : bool, default=False
           Whether to return categorical variables as strings.

       **return_X_y** : boolean, default=False.
           If True, returns ``(data.data, data.target)`` instead of a Bunch
           object.

       **as_frame** : boolean, default=False
           If True features are returned as pandas DataFrame.  If False
           features are returned as object or float array.  Float array
           is returned if all features are floats.



   :Returns:

       **dataset** : dict-like object with the following attributes:
           ..

       **dataset.data** : numpy array
           Each row corresponds to the features in the dataset.

       **dataset.target** : numpy array
           Each value is 1 if a purchase was made 0 otherwise.

       **dataset.DESCR** : string
           Description of the dataset.

       **(data, target_conversion, target_visit, target_exposure)** : tuple if
           ``return_X_y`` is True











   ..
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