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


================
混淆矩阵
================

使用混淆矩阵评估分类器在鸢尾花数据集上的输出质量示例。对角线元素表示预测标签等于真实标签的点的数量，而非对角线元素则表示分类器错误标记的点。混淆矩阵的对角线值越高越好，表明正确预测的数量多。

图中显示了按类别支持大小（每个类别中的元素数量）进行归一化和未归一化的混淆矩阵。在类别不平衡的情况下，这种归一化可以更直观地解释哪个类别被错误分类。

这里的结果不如预期，因为我们选择的正则化参数C不是最佳的。在实际应用中，这个参数通常使用 :ref:`grid_search` 选择。

.. GENERATED FROM PYTHON SOURCE LINES 13-55



.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_confusion_matrix_001.png
         :alt: Confusion matrix, without normalization
         :srcset: /auto_examples/model_selection/images/sphx_glr_plot_confusion_matrix_001.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_confusion_matrix_002.png
         :alt: Normalized confusion matrix
         :srcset: /auto_examples/model_selection/images/sphx_glr_plot_confusion_matrix_002.png
         :class: sphx-glr-multi-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none

    Confusion matrix, without normalization
    [[13  0  0]
     [ 0 10  6]
     [ 0  0  9]]
    Normalized confusion matrix
    [[1.   0.   0.  ]
     [0.   0.62 0.38]
     [0.   0.   1.  ]]






|

.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import datasets, svm
    from sklearn.metrics import ConfusionMatrixDisplay
    from sklearn.model_selection import train_test_split

    # 导入一些数据来玩玩
    iris = datasets.load_iris()
    X = iris.data
    y = iris.target
    class_names = iris.target_names

    # 将数据分为训练集和测试集
    X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)

    # 运行分类器，使用正则化过度的模型（C 值过低）以观察其对结果的影响
    classifier = svm.SVC(kernel="linear", C=0.01).fit(X_train, y_train)

    np.set_printoptions(precision=2)

    # 绘制未归一化的混淆矩阵
    titles_options = [
        ("Confusion matrix, without normalization", None),
        ("Normalized confusion matrix", "true"),
    ]
    for title, normalize in titles_options:
        disp = ConfusionMatrixDisplay.from_estimator(
            classifier,
            X_test,
            y_test,
            display_labels=class_names,
            cmap=plt.cm.Blues,
            normalize=normalize,
        )
        disp.ax_.set_title(title)

        print(title)
        print(disp.confusion_matrix)

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.092 seconds)


.. _sphx_glr_download_auto_examples_model_selection_plot_confusion_matrix.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/model_selection/plot_confusion_matrix.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_confusion_matrix.recommendations


.. only:: html

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

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