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


===========================================================
绘制岭回归系数与正则化参数的关系
===========================================================

展示了共线性对估计器系数的影响。

.. currentmodule:: sklearn.linear_model

:class:`Ridge` 回归是此示例中使用的估计器。
每种颜色代表系数向量的不同特征，并显示为正则化参数的函数。

此示例还展示了将岭回归应用于高度病态矩阵的有用性。
对于此类矩阵，目标变量的轻微变化可能会导致计算权重的巨大方差。
在这种情况下，设置一定的正则化参数（alpha）以减少这种变化（噪声）是有用的。

当 alpha 非常大时，正则化效应主导平方损失函数，系数趋于零。
在路径的末端，随着 alpha 趋向于零，解趋向于普通最小二乘法，系数表现出大的波动。
在实践中，有必要调整 alpha 以在两者之间保持平衡。

.. GENERATED FROM PYTHON SOURCE LINES 22-35

.. code-block:: Python


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

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import linear_model

    # X is the 10x10 Hilbert matrix
    X = 1.0 / (np.arange(1, 11) + np.arange(0, 10)[:, np.newaxis])
    y = np.ones(10)








.. GENERATED FROM PYTHON SOURCE LINES 36-38

计算路径
-------------

.. GENERATED FROM PYTHON SOURCE LINES 38-49

.. code-block:: Python



    n_alphas = 200
    alphas = np.logspace(-10, -2, n_alphas)

    coefs = []
    for a in alphas:
        ridge = linear_model.Ridge(alpha=a, fit_intercept=False)
        ridge.fit(X, y)
        coefs.append(ridge.coef_)








.. GENERATED FROM PYTHON SOURCE LINES 50-52

显示结果
---------------

.. GENERATED FROM PYTHON SOURCE LINES 52-64

.. code-block:: Python



    ax = plt.gca()

    ax.plot(alphas, coefs)
    ax.set_xscale("log")
    ax.set_xlim(ax.get_xlim()[::-1])  # reverse axis
    plt.xlabel("alpha")
    plt.ylabel("weights")
    plt.title("Ridge coefficients as a function of the regularization")
    plt.axis("tight")
    plt.show()



.. image-sg:: /auto_examples/linear_model/images/sphx_glr_plot_ridge_path_001.png
   :alt: Ridge coefficients as a function of the regularization
   :srcset: /auto_examples/linear_model/images/sphx_glr_plot_ridge_path_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.130 seconds)


.. _sphx_glr_download_auto_examples_linear_model_plot_ridge_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_ridge_path.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_ridge_path.recommendations


.. only:: html

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

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