.. 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 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.



.. image:: /auto_examples/ensemble/images/sphx_glr_plot_random_forest_regression_multioutput_001.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none


    /Users/thomasfan/Documents/scikit-learn-bootstrap/sklearn/base.py:420: FutureWarning: The default value of multioutput (not exposed in score method) will change from 'variance_weighted' to 'uniform_average' in 0.23 to keep consistent with 'metrics.r2_score'. To specify the default value manually and avoid the warning, please either call 'metrics.r2_score' directly or make a custom scorer with 'metrics.make_scorer' (the built-in scorer 'r2' uses multioutput='uniform_average').
      "multioutput='uniform_average').", FutureWarning)





|


.. 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  0.383 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:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/master?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_random_forest_regression_multioutput.ipynb
      :width: 150 px


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

     :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>`_