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

    Click :ref:`here <sphx_glr_download_auto_examples_ensemble_plot_voting_decision_regions.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ensemble_plot_voting_decision_regions.py:


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

Plot the decision boundaries of a `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
`VotingClassifier`.

First, three exemplary classifiers are initialized (`DecisionTreeClassifier`,
`KNeighborsClassifier`, and `SVC`) and used to initialize a
soft-voting `VotingClassifier` with weights `[2, 1, 2]`, which means that
the predicted probabilities of the `DecisionTreeClassifier` and `SVC`
count 5 times as much as the weights of the `KNeighborsClassifier` classifier
when the averaged probability is calculated.




.. code-block:: python

    print(__doc__)

    from itertools import product

    import numpy as np
    import matplotlib.pyplot as plt

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

    # 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(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
    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),
                         np.arange(y_min, y_max, 0.1))

    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']):

        Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
        Z = Z.reshape(xx.shape)

        axarr[idx[0], idx[1]].contourf(xx, yy, Z, alpha=0.4)
        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()

**Total running time of the script:** ( 0 minutes  0.000 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_voting_decision_regions.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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