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


=========================================================
逻辑回归三分类器
=========================================================

下图展示了逻辑回归分类器在 `iris <https://en.wikipedia.org/wiki/Iris_flower_data_set>`_  数据集的前两个维度（花萼长度和宽度）上的决策边界。数据点根据其标签进行着色。

.. GENERATED FROM PYTHON SOURCE LINES 9-51



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_iris_logistic_001.png
   :alt: plot iris logistic
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_iris_logistic_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # 代码来源：Gaël Varoquaux
    # 由Jaques Grobler修改用于文档
    # SPDX许可证标识符：BSD-3-Clause

    import matplotlib.pyplot as plt

    from sklearn import datasets
    from sklearn.inspection import DecisionBoundaryDisplay
    from sklearn.linear_model import LogisticRegression

    # 导入一些数据来玩玩
    iris = datasets.load_iris()
    X = iris.data[:, :2]  # we only take the first two features.
    Y = iris.target

    # 创建一个逻辑回归分类器实例并拟合数据。
    logreg = LogisticRegression(C=1e5)
    logreg.fit(X, Y)

    _, ax = plt.subplots(figsize=(4, 3))
    DecisionBoundaryDisplay.from_estimator(
        logreg,
        X,
        cmap=plt.cm.Paired,
        ax=ax,
        response_method="predict",
        plot_method="pcolormesh",
        shading="auto",
        xlabel="Sepal length",
        ylabel="Sepal width",
        eps=0.5,
    )

    # 还要绘制训练点
    plt.scatter(X[:, 0], X[:, 1], c=Y, edgecolors="k", cmap=plt.cm.Paired)


    plt.xticks(())
    plt.yticks(())

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.023 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_iris_logistic.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/linear_model/plot_iris_logistic.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_iris_logistic.recommendations


.. only:: html

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

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