.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/tree/plot_tree_regression_multioutput.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_tree_plot_tree_regression_multioutput.py>`
        to download the full example code or to run this example in your browser via JupyterLite or Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_tree_plot_tree_regression_multioutput.py:


===================================================================
Multi-output Decision Tree Regression
===================================================================

An example to illustrate multi-output regression with decision tree.

The :ref:`decision trees <tree>`
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.

.. GENERATED FROM PYTHON SOURCE LINES 17-66



.. image-sg:: /auto_examples/tree/images/sphx_glr_plot_tree_regression_multioutput_001.png
   :alt: Multi-output Decision Tree Regression
   :srcset: /auto_examples/tree/images/sphx_glr_plot_tree_regression_multioutput_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.tree import DecisionTreeRegressor

    # Create a random dataset
    rng = np.random.RandomState(1)
    X = np.sort(200 * rng.rand(100, 1) - 100, axis=0)
    y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
    y[::5, :] += 0.5 - rng.rand(20, 2)

    # Fit regression model
    regr_1 = DecisionTreeRegressor(max_depth=2)
    regr_2 = DecisionTreeRegressor(max_depth=5)
    regr_3 = DecisionTreeRegressor(max_depth=8)
    regr_1.fit(X, y)
    regr_2.fit(X, y)
    regr_3.fit(X, y)

    # Predict
    X_test = np.arange(-100.0, 100.0, 0.01)[:, np.newaxis]
    y_1 = regr_1.predict(X_test)
    y_2 = regr_2.predict(X_test)
    y_3 = regr_3.predict(X_test)

    # Plot the results
    plt.figure()
    s = 25
    plt.scatter(y[:, 0], y[:, 1], c="navy", s=s, edgecolor="black", label="data")
    plt.scatter(
        y_1[:, 0],
        y_1[:, 1],
        c="cornflowerblue",
        s=s,
        edgecolor="black",
        label="max_depth=2",
    )
    plt.scatter(y_2[:, 0], y_2[:, 1], c="red", s=s, edgecolor="black", label="max_depth=5")
    plt.scatter(
        y_3[:, 0], y_3[:, 1], c="orange", s=s, edgecolor="black", label="max_depth=8"
    )
    plt.xlim([-6, 6])
    plt.ylim([-6, 6])
    plt.xlabel("target 1")
    plt.ylabel("target 2")
    plt.title("Multi-output Decision Tree Regression")
    plt.legend(loc="best")
    plt.show()


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.219 seconds)


.. _sphx_glr_download_auto_examples_tree_plot_tree_regression_multioutput.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: binder-badge

      .. image:: images/binder_badge_logo.svg
        :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/main?urlpath=lab/tree/notebooks/auto_examples/tree/plot_tree_regression_multioutput.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/?path=auto_examples/tree/plot_tree_regression_multioutput.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_tree_regression_multioutput.ipynb <plot_tree_regression_multioutput.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_tree_regression_multioutput.py <plot_tree_regression_multioutput.py>`


.. include:: plot_tree_regression_multioutput.recommendations


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_