########################################### Multi-target prediction (``multitarget``) ########################################### .. toctree:: :maxdepth: 1 :hidden: Orange.multitarget.tree Orange.multitarget.binary Orange.multitarget.chain Orange.multitarget.neural Orange.multitarget.pls Orange.regression.earth 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` * :doc:`Orange.regression.earth` 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