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

    Click :ref:`here <sphx_glr_download_auto_examples_model_selection_plot_validation_curve.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_model_selection_plot_validation_curve.py:


==========================
Plotting Validation Curves
==========================

In this plot you can see the training scores and validation scores of an SVM
for different values of the kernel parameter gamma. For very low values of
gamma, you can see that both the training score and the validation score are
low. This is called underfitting. Medium values of gamma will result in high
values for both scores, i.e. the classifier is performing fairly well. If gamma
is too high, the classifier will overfit, which means that the training score
is good but the validation score is poor.



.. image:: /auto_examples/model_selection/images/sphx_glr_plot_validation_curve_001.png
    :class: sphx-glr-single-img






.. code-block:: default

    print(__doc__)

    import matplotlib.pyplot as plt
    import numpy as np

    from sklearn.datasets import load_digits
    from sklearn.svm import SVC
    from sklearn.model_selection import validation_curve

    X, y = load_digits(return_X_y=True)

    param_range = np.logspace(-6, -1, 5)
    train_scores, test_scores = validation_curve(
        SVC(), X, y, param_name="gamma", param_range=param_range,
        scoring="accuracy", n_jobs=1)
    train_scores_mean = np.mean(train_scores, axis=1)
    train_scores_std = np.std(train_scores, axis=1)
    test_scores_mean = np.mean(test_scores, axis=1)
    test_scores_std = np.std(test_scores, axis=1)

    plt.title("Validation Curve with SVM")
    plt.xlabel(r"$\gamma$")
    plt.ylabel("Score")
    plt.ylim(0.0, 1.1)
    lw = 2
    plt.semilogx(param_range, train_scores_mean, label="Training score",
                 color="darkorange", lw=lw)
    plt.fill_between(param_range, train_scores_mean - train_scores_std,
                     train_scores_mean + train_scores_std, alpha=0.2,
                     color="darkorange", lw=lw)
    plt.semilogx(param_range, test_scores_mean, label="Cross-validation score",
                 color="navy", lw=lw)
    plt.fill_between(param_range, test_scores_mean - test_scores_std,
                     test_scores_mean + test_scores_std, alpha=0.2,
                     color="navy", lw=lw)
    plt.legend(loc="best")
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  13.827 seconds)

**Estimated memory usage:**  8 MB


.. _sphx_glr_download_auto_examples_model_selection_plot_validation_curve.py:


.. only :: html

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


  .. container:: binder-badge

    .. image:: https://mybinder.org/badge_logo.svg
      :target: https://mybinder.org/v2/gh/scikit-learn/scikit-learn/0.22.X?urlpath=lab/tree/notebooks/auto_examples/model_selection/plot_validation_curve.ipynb
      :width: 150 px


  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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