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orange-multitarget / docs / rst / Orange.multitarget.rst

Aleš Erjavec f679982 
Miran Levar 53f9b79 
Aleš Erjavec f679982 
Miran Levar 53f9b79 


Miran Levar 14d5bd1 
Miran Levar 53f9b79 




Miran Levar 6c0af23 
Miran Levar 14d5bd1 




Miran Levar c1969b8 

Miran Levar 14d5bd1 
Aleš Erjavec f679982 
Miran Levar 14d5bd1 




Miran Levar 6c0af23 
Aleš Erjavec f679982 



Miran Levar 53f9b79 

Miran Levar 14d5bd1 
Miran Levar 53f9b79 

Miran Levar 6c0af23 











Miran Levar 53f9b79 
#########################################
Multi-target prediction (``multitarget``)
#########################################

.. toctree::
   :maxdepth: 1
   :hidden:

   Orange.multitarget.tree
   Orange.multitarget.binary
   Orange.multitarget.chain
   Orange.multitarget.neural
   Orange.multitarget.pls
   Orange.multitarget.scoring


Multi-target prediction tries to achieve better prediction accuracy or speed
through prediction of multiple dependent variables at once. It works on
multi-target data, which is also supported by
Orange's tab file format using multiclass directive.

List of supported learners:

* :doc:`Orange.multitarget.tree`
* :doc:`Orange.multitarget.binary`
* :doc:`Orange.multitarget.chain`
* :doc:`Orange.multitarget.neural`
* :doc:`Orange.multitarget.pls`

Additionally :class:`orangecontrib.earth.EarthLearner` from the
`orangecontrib.earth <https://pypi.python.org/pypi/orangecontrib.earth>`_
package is also supports multi-target predictions.

For evaluation of multi-target methods, see the corresponding section in 
:doc:`Orange.multitarget.scoring`.


The addon also includes three sample datasets:

* **bridges.tab** - dataset with 5 multi-class class variables
* **flare.tab** - dataset with 3 multi-class class variables
* **emotions.tab** - dataset with 6 binary class variables (a multi-label dataset)

Example of loading an included dataset:

.. literalinclude:: code/multitarget.py
    :lines: 1-2


.. automodule:: Orange.multitarget