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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_examples_ensemble_plot_random_forest_regression_multioutput.py>`
        to download the full example code or to run this example in your browser via Binder

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

.. _sphx_glr_auto_examples_ensemble_plot_random_forest_regression_multioutput.py:


============================================================
Comparing random forests and the multi-output meta estimator
============================================================

An example to compare multi-output regression with random forest and
the :ref:`multioutput.MultiOutputRegressor <multiclass>` meta-estimator.

This example illustrates the use of the
:ref:`multioutput.MultiOutputRegressor <multiclass>` meta-estimator
to perform multi-output regression. A random forest regressor is used,
which supports multi-output regression natively, so the results can be
compared.

The random forest regressor will only ever predict values within the
range of observations or closer to zero for each of the targets. As a
result the predictions are biased towards the centre of the circle.

Using a single underlying feature the model learns both the
x and y coordinate as output.

.. GENERATED FROM PYTHON SOURCE LINES 23-78



.. image:: /auto_examples/ensemble/images/sphx_glr_plot_random_forest_regression_multioutput_001.png
    :alt: Comparing random forests and the multi-output meta estimator
    :class: sphx-glr-single-img





.. code-block:: default

    print(__doc__)

    # Author: Tim Head <betatim@gmail.com>
    #
    # License: BSD 3 clause

    import numpy as np
    import matplotlib.pyplot as plt
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.model_selection import train_test_split
    from sklearn.multioutput import MultiOutputRegressor


    # Create a random dataset
    rng = np.random.RandomState(1)
    X = np.sort(200 * rng.rand(600, 1) - 100, axis=0)
    y = np.array([np.pi * np.sin(X).ravel(), np.pi * np.cos(X).ravel()]).T
    y += (0.5 - rng.rand(*y.shape))

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, train_size=400, test_size=200, random_state=4)

    max_depth = 30
    regr_multirf = MultiOutputRegressor(RandomForestRegressor(n_estimators=100,
                                                              max_depth=max_depth,
                                                              random_state=0))
    regr_multirf.fit(X_train, y_train)

    regr_rf = RandomForestRegressor(n_estimators=100, max_depth=max_depth,
                                    random_state=2)
    regr_rf.fit(X_train, y_train)

    # Predict on new data
    y_multirf = regr_multirf.predict(X_test)
    y_rf = regr_rf.predict(X_test)

    # Plot the results
    plt.figure()
    s = 50
    a = 0.4
    plt.scatter(y_test[:, 0], y_test[:, 1], edgecolor='k',
                c="navy", s=s, marker="s", alpha=a, label="Data")
    plt.scatter(y_multirf[:, 0], y_multirf[:, 1], edgecolor='k',
                c="cornflowerblue", s=s, alpha=a,
                label="Multi RF score=%.2f" % regr_multirf.score(X_test, y_test))
    plt.scatter(y_rf[:, 0], y_rf[:, 1], edgecolor='k',
                c="c", s=s, marker="^", alpha=a,
                label="RF score=%.2f" % regr_rf.score(X_test, y_test))
    plt.xlim([-6, 6])
    plt.ylim([-6, 6])
    plt.xlabel("target 1")
    plt.ylabel("target 2")
    plt.title("Comparing random forests and the multi-output meta estimator")
    plt.legend()
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.623 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_random_forest_regression_multioutput.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: images/binder_badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.24.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb
      :alt: Launch binder
      :width: 150 px


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

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



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

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


.. only:: html

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

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