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


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

.. currentmodule:: sklearn

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:`~linear_model.LogisticRegression`, :class:`~naive_bayes.GaussianNB`,
and :class:`~ensemble.RandomForestClassifier`) and used to initialize a
soft-voting :class:`~ensemble.VotingClassifier` with weights `[1, 1, 5]`, which
means that the predicted probabilities of the
:class:`~ensemble.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.

.. GENERATED FROM PYTHON SOURCE LINES 25-98



.. image-sg:: /auto_examples/ensemble/images/sphx_glr_plot_voting_probas_001.png
   :alt: Class probabilities for sample 1 by different classifiers
   :srcset: /auto_examples/ensemble/images/sphx_glr_plot_voting_probas_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


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

    import matplotlib.pyplot as plt
    import numpy as np

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

    clf1 = LogisticRegression(max_iter=1000, random_state=123)
    clf2 = RandomForestClassifier(n_estimators=100, 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.tight_layout()
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.327 seconds)


.. _sphx_glr_download_auto_examples_ensemble_plot_voting_probas.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_probas.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_probas.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_voting_probas.recommendations


.. only:: html

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

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