.. 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 Binder

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_ensemble_plot_voting_probas.py:


===========================================================
绘制由VotingClassifier计算的类别概率
===========================================================

.. currentmodule:: sklearn

绘制玩具数据集中第一个样本的类别概率，该概率由三个不同的分类器预测并由
:class:`~ensemble.VotingClassifier` 平均。

首先，初始化三个示例性分类器
(:class:`~linear_model.LogisticRegression` , :class:`~naive_bayes.GaussianNB` ,
和 :class:`~ensemble.RandomForestClassifier` )，并使用权重 `[1, 1, 5]` 初始化一个
软投票 :class:`~ensemble.VotingClassifier` ，这意味着在计算平均概率时，
:class:`~ensemble.RandomForestClassifier` 的预测概率的权重是其他分类器的5倍。

为了可视化概率加权，我们将每个分类器拟合到训练集上，并绘制该示例数据集中第一个样本的预测类别概率。

.. GENERATED FROM PYTHON SOURCE LINES 20-90



.. 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


    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],
    )

    # 预测所有分类器的类别概率
    probas = [c.fit(X, y).predict_proba(X) for c in (clf1, clf2, clf3, eclf)]

    # 获取数据集中第一个样本的类别概率
    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()

    # 分类器1-3的条形图
    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"
    )

    # 图注
    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 0.140 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/main?urlpath=lab/tree/notebooks/auto_examples/ensemble/plot_voting_probas.ipynb
        :alt: Launch binder
        :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>`_