
.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ensemble/plot_voting_regressor.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_ensemble_plot_voting_regressor.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_ensemble_plot_voting_regressor.py:


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

.. currentmodule:: sklearn

A voting regressor is an ensemble meta-estimator that fits several base
regressors, each on the whole dataset. Then it averages the individual
predictions to form a final prediction.
We will use three different regressors to predict the data:
:class:`~ensemble.GradientBoostingRegressor`,
:class:`~ensemble.RandomForestRegressor`, and
:class:`~linear_model.LinearRegression`).
Then the above 3 regressors will be used for the
:class:`~ensemble.VotingRegressor`.

Finally, we will plot the predictions made by all models for comparison.

We will work with the diabetes dataset which consists of 10 features
collected from a cohort of diabetes patients. The target is a quantitative
measure of disease progression one year after baseline.

.. GENERATED FROM PYTHON SOURCE LINES 25-36

.. code-block:: Python


    import matplotlib.pyplot as plt

    from sklearn.datasets import load_diabetes
    from sklearn.ensemble import (
        GradientBoostingRegressor,
        RandomForestRegressor,
        VotingRegressor,
    )
    from sklearn.linear_model import LinearRegression








.. GENERATED FROM PYTHON SOURCE LINES 37-43

Training classifiers
--------------------------------

First, we will load the diabetes dataset and initiate a gradient boosting
regressor, a random forest regressor and a linear regression. Next, we will
use the 3 regressors to build the voting regressor:

.. GENERATED FROM PYTHON SOURCE LINES 43-58

.. code-block:: Python


    X, y = load_diabetes(return_X_y=True)

    # Train classifiers
    reg1 = GradientBoostingRegressor(random_state=1)
    reg2 = RandomForestRegressor(random_state=1)
    reg3 = LinearRegression()

    reg1.fit(X, y)
    reg2.fit(X, y)
    reg3.fit(X, y)

    ereg = VotingRegressor([("gb", reg1), ("rf", reg2), ("lr", reg3)])
    ereg.fit(X, y)






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    - Pipeline and ColumnTransformer use this feature and define the default style
    - Estimators will overwrite some part of the style using the `sk-estimator` class
    */

    /* Pipeline and ColumnTransformer style (default) */

    #sk-container-id-14 div.sk-toggleable {
      /* Default theme specific background. It is overwritten whether we have a
      specific estimator or a Pipeline/ColumnTransformer */
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    }

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    #sk-container-id-14 label.sk-toggleable__label {
      cursor: pointer;
      display: block;
      width: 100%;
      margin-bottom: 0;
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      box-sizing: border-box;
      text-align: center;
    }

    #sk-container-id-14 label.sk-toggleable__label-arrow:before {
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      overflow: hidden;
      text-align: left;
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      color: var(--sklearn-color-background);
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    .sk-estimator-doc-link.fitted:hover,
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      color: var(--sklearn-color-background);
      text-decoration: none;
    }

    /* Span, style for the box shown on hovering the info icon */
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      display: none;
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      float: right;
      font-size: 1rem;
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      background-color: var(--sklearn-color-background);
      border-radius: 1rem;
      height: 1rem;
      width: 1rem;
      text-decoration: none;
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      /* fitted */
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      /* unfitted */
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    #sk-container-id-14 a.estimator_doc_link.fitted:hover {
      /* fitted */
      background-color: var(--sklearn-color-fitted-level-3);
    }
    </style><div id="sk-container-id-14" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>VotingRegressor(estimators=[(&#x27;gb&#x27;, GradientBoostingRegressor(random_state=1)),
                                (&#x27;rf&#x27;, RandomForestRegressor(random_state=1)),
                                (&#x27;lr&#x27;, LinearRegression())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-34" type="checkbox" ><label for="sk-estimator-id-34" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;VotingRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.VotingRegressor.html">?<span>Documentation for VotingRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>VotingRegressor(estimators=[(&#x27;gb&#x27;, GradientBoostingRegressor(random_state=1)),
                                (&#x27;rf&#x27;, RandomForestRegressor(random_state=1)),
                                (&#x27;lr&#x27;, LinearRegression())])</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>gb</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-35" type="checkbox" ><label for="sk-estimator-id-35" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;GradientBoostingRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.GradientBoostingRegressor.html">?<span>Documentation for GradientBoostingRegressor</span></a></label><div class="sk-toggleable__content fitted"><pre>GradientBoostingRegressor(random_state=1)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>rf</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-36" type="checkbox" ><label for="sk-estimator-id-36" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor(random_state=1)</pre></div> </div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label fitted sk-toggleable"><label>lr</label></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-37" type="checkbox" ><label for="sk-estimator-id-37" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;LinearRegression<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html">?<span>Documentation for LinearRegression</span></a></label><div class="sk-toggleable__content fitted"><pre>LinearRegression()</pre></div> </div></div></div></div></div></div></div></div></div>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 59-63

Making predictions
--------------------------------

Now we will use each of the regressors to make the 20 first predictions.

.. GENERATED FROM PYTHON SOURCE LINES 63-71

.. code-block:: Python


    xt = X[:20]

    pred1 = reg1.predict(xt)
    pred2 = reg2.predict(xt)
    pred3 = reg3.predict(xt)
    pred4 = ereg.predict(xt)








.. GENERATED FROM PYTHON SOURCE LINES 72-77

Plot the results
--------------------------------

Finally, we will visualize the 20 predictions. The red stars show the average
prediction made by :class:`~ensemble.VotingRegressor`.

.. GENERATED FROM PYTHON SOURCE LINES 77-91

.. code-block:: Python


    plt.figure()
    plt.plot(pred1, "gd", label="GradientBoostingRegressor")
    plt.plot(pred2, "b^", label="RandomForestRegressor")
    plt.plot(pred3, "ys", label="LinearRegression")
    plt.plot(pred4, "r*", ms=10, 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("Regressor predictions and their average")

    plt.show()



.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_voting_regressor_001.png
   :alt: Regressor predictions and their average
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_voting_regressor_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.897 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_voting_regressor.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_voting_regressor.zip <plot_voting_regressor.zip>`


.. only:: html

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

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