.. _example_tree_plot_tree_regression_multioutput.py:
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Multi-output Decision Tree Regression
===================================================================
An example to illustrate multi-output regression with decision tree.
The :ref:`decision trees `
is used to predict simultaneously the noisy x and y observations of a circle
given a single underlying feature. As a result, it learns local linear
regressions approximating the circle.
We can see that if the maximum depth of the tree (controlled by the
`max_depth` parameter) is set too high, the decision trees learn too fine
details of the training data and learn from the noise, i.e. they overfit.
.. image:: images/plot_tree_regression_multioutput_001.png
:align: center
**Python source code:** :download:`plot_tree_regression_multioutput.py `
.. literalinclude:: plot_tree_regression_multioutput.py
:lines: 17-
**Total running time of the example:** 0.41 seconds
( 0 minutes 0.41 seconds)