.. 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 ` to download the full example code. or to run this example in your browser via JupyterLite or 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 25-39 .. code-block:: Python # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause 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 40-46 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 46-61 .. 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) .. raw:: html

VotingRegressor(estimators=[('gb', GradientBoostingRegressor(random_state=1)),
                                ('rf', RandomForestRegressor(random_state=1)),
                                ('lr', LinearRegression())])

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.. GENERATED FROM PYTHON SOURCE LINES 62-66 Making predictions -------------------------------- Now we will use each of the regressors to make the 20 first predictions. .. GENERATED FROM PYTHON SOURCE LINES 66-74 .. 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 75-80 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 80-94 .. 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 0.966 seconds) .. _sphx_glr_download_auto_examples_ensemble_plot_voting_regressor.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/1.6.X?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_voting_regressor.ipynb :alt: Launch binder :width: 150 px .. container:: lite-badge .. image:: images/jupyterlite_badge_logo.svg :target: ../../lite/lab/index.html?path=auto_examples/ensemble/plot_voting_regressor.ipynb :alt: Launch JupyterLite :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_voting_regressor.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_voting_regressor.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_voting_regressor.zip ` .. include:: plot_voting_regressor.recommendations .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_