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


==============================================
L1-正则化路径的逻辑回归
==============================================

在从鸢尾花数据集中提取的二分类问题上训练带有L1惩罚项的逻辑回归模型。

这些模型按从最强正则化到最弱正则化的顺序排列。收集并绘制模型的4个系数作为“正则化路径”：在图的左侧（强正则化器），所有系数都为0。当正则化逐渐减弱时，系数会一个接一个地变为非零值。

在这里我们选择liblinear求解器，因为它可以有效地优化带有非平滑、稀疏诱导的L1惩罚项的逻辑回归损失。

还要注意，我们设置了一个较低的容差值，以确保在收集系数之前模型已经收敛。

我们还使用了warm_start=True，这意味着模型的系数被重用以初始化下一个模型拟合，从而加快全路径的计算。

.. GENERATED FROM PYTHON SOURCE LINES 17-21

.. code-block:: Python


    # 作者：scikit-learn 开发者
    # SPDX-License-Identifier: BSD-3-Clause








.. GENERATED FROM PYTHON SOURCE LINES 22-24

Load data
---------

.. GENERATED FROM PYTHON SOURCE LINES 24-36

.. code-block:: Python


    from sklearn import datasets

    iris = datasets.load_iris()
    X = iris.data
    y = iris.target

    X = X[y != 2]
    y = y[y != 2]

    X /= X.max()  # Normalize X to speed-up convergence








.. GENERATED FROM PYTHON SOURCE LINES 37-39

计算正则化路径
---------------------------

.. GENERATED FROM PYTHON SOURCE LINES 39-64

.. code-block:: Python



    import numpy as np

    from sklearn import linear_model
    from sklearn.svm import l1_min_c

    cs = l1_min_c(X, y, loss="log") * np.logspace(0, 10, 16)

    clf = linear_model.LogisticRegression(
        penalty="l1",
        solver="liblinear",
        tol=1e-6,
        max_iter=int(1e6),
        warm_start=True,
        intercept_scaling=10000.0,
    )
    coefs_ = []
    for c in cs:
        clf.set_params(C=c)
        clf.fit(X, y)
        coefs_.append(clf.coef_.ravel().copy())

    coefs_ = np.array(coefs_)








.. GENERATED FROM PYTHON SOURCE LINES 65-67

绘制正则化路径
------------------------

.. GENERATED FROM PYTHON SOURCE LINES 67-78

.. code-block:: Python



    import matplotlib.pyplot as plt

    plt.plot(np.log10(cs), coefs_, marker="o")
    ymin, ymax = plt.ylim()
    plt.xlabel("log(C)")
    plt.ylabel("Coefficients")
    plt.title("Logistic Regression Path")
    plt.axis("tight")
    plt.show()



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_logistic_path_001.png
   :alt: Logistic Regression Path
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_logistic_path_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.052 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_logistic_path.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_logistic_path.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_logistic_path.recommendations


.. only:: html

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

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