.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "packages/scikit-learn/auto_examples/plot_linear_model_cv.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_packages_scikit-learn_auto_examples_plot_linear_model_cv.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_packages_scikit-learn_auto_examples_plot_linear_model_cv.py:


================================================================
Use the RidgeCV and LassoCV to set the regularization parameter
================================================================

.. GENERATED FROM PYTHON SOURCE LINES 10-11

Load the diabetes dataset

.. GENERATED FROM PYTHON SOURCE LINES 11-17

.. code-block:: Python

    from sklearn.datasets import load_diabetes

    data = load_diabetes()
    X, y = data.data, data.target
    print(X.shape)





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

 .. code-block:: none

    (442, 10)




.. GENERATED FROM PYTHON SOURCE LINES 18-19

Compute the cross-validation score with the default hyper-parameters

.. GENERATED FROM PYTHON SOURCE LINES 19-26

.. code-block:: Python

    from sklearn.model_selection import cross_val_score
    from sklearn.linear_model import Ridge, Lasso

    for Model in [Ridge, Lasso]:
        model = Model()
        print(f"{Model.__name__}: {cross_val_score(model, X, y).mean()}")





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

 .. code-block:: none

    Ridge: 0.410174971340889
    Lasso: 0.3375593674654274




.. GENERATED FROM PYTHON SOURCE LINES 27-29

We compute the cross-validation score as a function of alpha, the
strength of the regularization for Lasso and Ridge

.. GENERATED FROM PYTHON SOURCE LINES 29-45

.. code-block:: Python

    import numpy as np
    import matplotlib.pyplot as plt

    alphas = np.logspace(-3, -1, 30)

    plt.figure(figsize=(5, 3))

    for Model in [Lasso, Ridge]:
        scores = [cross_val_score(Model(alpha), X, y, cv=3).mean() for alpha in alphas]
        plt.plot(alphas, scores, label=Model.__name__)

    plt.legend(loc="lower left")
    plt.xlabel("alpha")
    plt.ylabel("cross validation score")
    plt.tight_layout()
    plt.show()



.. image-sg:: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_linear_model_cv_001.png
   :alt: plot linear model cv
   :srcset: /packages/scikit-learn/auto_examples/images/sphx_glr_plot_linear_model_cv_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** (0 minutes 0.420 seconds)


.. _sphx_glr_download_packages_scikit-learn_auto_examples_plot_linear_model_cv.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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

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

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

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

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

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


.. only:: html

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

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