.. 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 Click :ref:`here ` 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 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 25-35 .. code-block:: default print(__doc__) import matplotlib.pyplot as plt from sklearn.datasets import load_diabetes from sklearn.ensemble import GradientBoostingRegressor from sklearn.ensemble import RandomForestRegressor from sklearn.linear_model import LinearRegression from sklearn.ensemble import VotingRegressor .. GENERATED FROM PYTHON SOURCE LINES 36-42 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 42-57 .. code-block:: default 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) .. raw:: html 
VotingRegressor(estimators=[('gb', GradientBoostingRegressor(random_state=1)),
                                ('rf', RandomForestRegressor(random_state=1)),
                                ('lr', LinearRegression())])
GradientBoostingRegressor(random_state=1)
RandomForestRegressor(random_state=1)
LinearRegression()


.. GENERATED FROM PYTHON SOURCE LINES 58-62 Making predictions -------------------------------- Now we will use each of the regressors to make the 20 first predictions. .. GENERATED FROM PYTHON SOURCE LINES 62-70 .. code-block:: default xt = X[:20] pred1 = reg1.predict(xt) pred2 = reg2.predict(xt) pred3 = reg3.predict(xt) pred4 = ereg.predict(xt) .. GENERATED FROM PYTHON SOURCE LINES 71-76 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 76-91 .. code-block:: default 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:: /auto_examples/ensemble/images/sphx_glr_plot_voting_regressor_001.png :alt: Regressor predictions and their average :class: sphx-glr-single-img .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 0.986 seconds) .. _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:: 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_voting_regressor.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_voting_regressor.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_voting_regressor.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_