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


===============================================
糖尿病数据集上的交叉验证练习
===============================================

一个使用线性模型进行交叉验证的教程练习。

此练习用于 :ref:`cv_estimators_tut` 部分的 :ref:`model_selection_tut` 章节中的 :ref:`stat_learn_tut_index` 。
    

.. GENERATED FROM PYTHON SOURCE LINES 14-16

加载数据集并应用网格搜索交叉验证
-----------------------------------

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.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn import datasets
    from sklearn.linear_model import Lasso
    from sklearn.model_selection import GridSearchCV

    X, y = datasets.load_diabetes(return_X_y=True)
    X = X[:150]
    y = y[:150]

    lasso = Lasso(random_state=0, max_iter=10000)
    alphas = np.logspace(-4, -0.5, 30)

    tuned_parameters = [{"alpha": alphas}]
    n_folds = 5

    clf = GridSearchCV(lasso, tuned_parameters, cv=n_folds, refit=False)
    clf.fit(X, y)
    scores = clf.cv_results_["mean_test_score"]
    scores_std = clf.cv_results_["std_test_score"]








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绘制显示分数正负标准误差的误差线
------------------------------------------------------

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.. code-block:: Python



    plt.figure().set_size_inches(8, 6)
    plt.semilogx(alphas, scores)

    std_error = scores_std / np.sqrt(n_folds)

    plt.semilogx(alphas, scores + std_error, "b--")
    plt.semilogx(alphas, scores - std_error, "b--")

    # alpha=0.2 控制填充颜色的透明度
    plt.fill_between(alphas, scores + std_error, scores - std_error, alpha=0.2)

    plt.ylabel("CV score +/- std error")
    plt.xlabel("alpha")
    plt.axhline(np.max(scores), linestyle="--", color=".5")
    plt.xlim([alphas[0], alphas[-1]])




.. image-sg:: /auto_examples/exercises/images/sphx_glr_plot_cv_diabetes_001.png
   :alt: plot cv diabetes
   :srcset: /auto_examples/exercises/images/sphx_glr_plot_cv_diabetes_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    (np.float64(0.0001), np.float64(0.31622776601683794))



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奖励：你对 alpha 的选择有多大信任度？
-----------------------------------------------------

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.. code-block:: Python


    # 为了回答这个问题，我们使用LassoCV对象，该对象通过内部交叉验证（即对接收到的训练数据执行交叉验证）自动从数据中设置其alpha参数。我们使用外部交叉验证来观察自动获得的alpha在不同交叉验证折叠中的差异。

    from sklearn.linear_model import LassoCV
    from sklearn.model_selection import KFold

    lasso_cv = LassoCV(alphas=alphas, random_state=0, max_iter=10000)
    k_fold = KFold(3)

    print("Answer to the bonus question:", "how much can you trust the selection of alpha?")
    print()
    print("Alpha parameters maximising the generalization score on different")
    print("subsets of the data:")
    for k, (train, test) in enumerate(k_fold.split(X, y)):
        lasso_cv.fit(X[train], y[train])
        print(
            "[fold {0}] alpha: {1:.5f}, score: {2:.5f}".format(
                k, lasso_cv.alpha_, lasso_cv.score(X[test], y[test])
            )
        )
    print()
    print("Answer: Not very much since we obtained different alphas for different")
    print("subsets of the data and moreover, the scores for these alphas differ")
    print("quite substantially.")

    plt.show()




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

 .. code-block:: none

    Answer to the bonus question: how much can you trust the selection of alpha?

    Alpha parameters maximising the generalization score on different
    subsets of the data:
    [fold 0] alpha: 0.05968, score: 0.54209
    [fold 1] alpha: 0.04520, score: 0.15521
    [fold 2] alpha: 0.07880, score: 0.45192

    Answer: Not very much since we obtained different alphas for different
    subsets of the data and moreover, the scores for these alphas differ
    quite substantially.





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

   **Total running time of the script:** (0 minutes 0.296 seconds)


.. _sphx_glr_download_auto_examples_exercises_plot_cv_diabetes.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/exercises/plot_cv_diabetes.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_cv_diabetes.recommendations


.. only:: html

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

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