.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/ensemble/plot_voting_decision_regions.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_decision_regions.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_decision_regions.py:


==================================================
Plot the decision boundaries of a VotingClassifier
==================================================

.. currentmodule:: sklearn

Plot the decision boundaries of a :class:`~ensemble.VotingClassifier` for two
features of the Iris dataset.

Plot the class probabilities of the first sample in a toy dataset predicted by
three different classifiers and averaged by the
:class:`~ensemble.VotingClassifier`.

First, three exemplary classifiers are initialized
(:class:`~tree.DecisionTreeClassifier`,
:class:`~neighbors.KNeighborsClassifier`, and :class:`~svm.SVC`) and used to
initialize a soft-voting :class:`~ensemble.VotingClassifier` with weights `[2,
1, 2]`, which means that the predicted probabilities of the
:class:`~tree.DecisionTreeClassifier` and :class:`~svm.SVC` each count 2 times
as much as the weights of the :class:`~neighbors.KNeighborsClassifier`
classifier when the averaged probability is calculated.

.. GENERATED FROM PYTHON SOURCE LINES 25-74



.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_voting_decision_regions_001.png
   :alt: Decision Tree (depth=4), KNN (k=7), Kernel SVM, Soft Voting
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_voting_decision_regions_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    from itertools import product

    import matplotlib.pyplot as plt

    from sklearn import datasets
    from sklearn.ensemble import VotingClassifier
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.neighbors import KNeighborsClassifier
    from sklearn.svm import SVC
    from sklearn.tree import DecisionTreeClassifier

    # Loading some example data
    iris = datasets.load_iris()
    X = iris.data[:, [0, 2]]
    y = iris.target

    # Training classifiers
    clf1 = DecisionTreeClassifier(max_depth=4)
    clf2 = KNeighborsClassifier(n_neighbors=7)
    clf3 = SVC(gamma=0.1, kernel="rbf", probability=True)
    eclf = VotingClassifier(
        estimators=[("dt", clf1), ("knn", clf2), ("svc", clf3)],
        voting="soft",
        weights=[2, 1, 2],
    )

    clf1.fit(X, y)
    clf2.fit(X, y)
    clf3.fit(X, y)
    eclf.fit(X, y)

    # Plotting decision regions
    f, axarr = plt.subplots(2, 2, sharex="col", sharey="row", figsize=(10, 8))
    for idx, clf, tt in zip(
        product([0, 1], [0, 1]),
        [clf1, clf2, clf3, eclf],
        ["Decision Tree (depth=4)", "KNN (k=7)", "Kernel SVM", "Soft Voting"],
    ):
        DecisionBoundaryDisplay.from_estimator(
            clf, X, alpha=0.4, ax=axarr[idx[0], idx[1]], response_method="predict"
        )
        axarr[idx[0], idx[1]].scatter(X[:, 0], X[:, 1], c=y, s=20, edgecolor="k")
        axarr[idx[0], idx[1]].set_title(tt)

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.618 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_voting_decision_regions.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/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_voting_decision_regions.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_voting_decision_regions.recommendations


.. only:: html

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

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