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

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

.. _sphx_glr_auto_examples_ensemble_plot_voting_probas.py:


===========================================================
Plot class probabilities calculated by the VotingClassifier
===========================================================

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

First, three examplary classifiers are initialized (`LogisticRegression`,
`GaussianNB`, and `RandomForestClassifier`) and used to initialize a
soft-voting `VotingClassifier` with weights `[1, 1, 5]`, which means that
the predicted probabilities of the `RandomForestClassifier` count 5 times
as much as the weights of the other classifiers when the averaged probability
is calculated.

To visualize the probability weighting, we fit each classifier on the training
set and plot the predicted class probabilities for the first sample in this
example dataset.




.. code-block:: python

    print(__doc__)

    import numpy as np
    import matplotlib.pyplot as plt

    from sklearn.linear_model import LogisticRegression
    from sklearn.naive_bayes import GaussianNB
    from sklearn.ensemble import RandomForestClassifier
    from sklearn.ensemble import VotingClassifier

    clf1 = LogisticRegression(random_state=123)
    clf2 = RandomForestClassifier(random_state=123)
    clf3 = GaussianNB()
    X = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
    y = np.array([1, 1, 2, 2])

    eclf = VotingClassifier(estimators=[('lr', clf1), ('rf', clf2), ('gnb', clf3)],
                            voting='soft',
                            weights=[1, 1, 5])

    # predict class probabilities for all classifiers
    probas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]

    # get class probabilities for the first sample in the dataset
    class1_1 = [pr[0, 0] for pr in probas]
    class2_1 = [pr[0, 1] for pr in probas]


    # plotting

    N = 4  # number of groups
    ind = np.arange(N)  # group positions
    width = 0.35  # bar width

    fig, ax = plt.subplots()

    # bars for classifier 1-3
    p1 = ax.bar(ind, np.hstack(([class1_1[:-1], [0]])), width,
                color='green', edgecolor='k')
    p2 = ax.bar(ind + width, np.hstack(([class2_1[:-1], [0]])), width,
                color='lightgreen', edgecolor='k')

    # bars for VotingClassifier
    p3 = ax.bar(ind, [0, 0, 0, class1_1[-1]], width,
                color='blue', edgecolor='k')
    p4 = ax.bar(ind + width, [0, 0, 0, class2_1[-1]], width,
                color='steelblue', edgecolor='k')

    # plot annotations
    plt.axvline(2.8, color='k', linestyle='dashed')
    ax.set_xticks(ind + width)
    ax.set_xticklabels(['LogisticRegression\nweight 1',
                        'GaussianNB\nweight 1',
                        'RandomForestClassifier\nweight 5',
                        'VotingClassifier\n(average probabilities)'],
                       rotation=40,
                       ha='right')
    plt.ylim([0, 1])
    plt.title('Class probabilities for sample 1 by different classifiers')
    plt.legend([p1[0], p2[0]], ['class 1', 'class 2'], loc='upper left')
    plt.show()

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


.. _sphx_glr_download_auto_examples_ensemble_plot_voting_probas.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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