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

    Click :ref:`here <sphx_glr_download_auto_examples_ensemble_plot_voting_regressor.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_voting_regressor.py:


=================================================
Plot individual and voting regression predictions
=================================================

.. currentmodule:: sklearn

Plot individual and averaged regression predictions for Boston dataset.

First, three exemplary regressors are initialized
(:class:`~ensemble.GradientBoostingRegressor`,
:class:`~ensemble.RandomForestRegressor`, and
:class:`~linear_model.LinearRegression`) and used to initialize a
:class:`~ensemble.VotingRegressor`.

The red starred dots are the averaged predictions.



.. image:: /auto_examples/ensemble/images/sphx_glr_plot_voting_regressor_001.png
    :class: sphx-glr-single-img






.. code-block:: default

    print(__doc__)

    import matplotlib.pyplot as plt

    from sklearn import datasets
    from sklearn.ensemble import GradientBoostingRegressor
    from sklearn.ensemble import RandomForestRegressor
    from sklearn.linear_model import LinearRegression
    from sklearn.ensemble import VotingRegressor

    # Loading some example data
    X, y = datasets.load_boston(return_X_y=True)

    # Training classifiers
    reg1 = GradientBoostingRegressor(random_state=1, n_estimators=10)
    reg2 = RandomForestRegressor(random_state=1, n_estimators=10)
    reg3 = LinearRegression()
    ereg = VotingRegressor([('gb', reg1), ('rf', reg2), ('lr', reg3)])
    reg1.fit(X, y)
    reg2.fit(X, y)
    reg3.fit(X, y)
    ereg.fit(X, y)

    xt = X[:20]

    plt.figure()
    plt.plot(reg1.predict(xt), 'gd', label='GradientBoostingRegressor')
    plt.plot(reg2.predict(xt), 'b^', label='RandomForestRegressor')
    plt.plot(reg3.predict(xt), 'ys', label='LinearRegression')
    plt.plot(ereg.predict(xt), 'r*', label='VotingRegressor')
    plt.tick_params(axis='x', which='both', bottom=False, top=False,
                    labelbottom=False)
    plt.ylabel('predicted')
    plt.xlabel('training samples')
    plt.legend(loc="best")
    plt.title('Comparison of individual predictions with averaged')
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  0.461 seconds)

**Estimated memory usage:**  8 MB


.. _sphx_glr_download_auto_examples_ensemble_plot_voting_regressor.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/0.22.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_voting_regressor.ipynb
      :width: 150 px


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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