.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/model_selection/plot_validation_curve.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_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:


==========================
绘制验证曲线
==========================

在此图中，您可以看到支持向量机（SVM）在不同核参数 gamma 值下的训练分数和验证分数。对于非常低的 gamma 值，您会发现训练分数和验证分数都很低。这被称为欠拟合。中等的 gamma 值将导致两个分数都很高，即分类器表现相当好。如果 gamma 值过高，分类器将会过拟合，这意味着训练分数很好但验证分数很差。

.. GENERATED FROM PYTHON SOURCE LINES 9-35



.. image-sg:: /auto_examples/model_selection/images/sphx_glr_plot_validation_curve_001.png
   :alt: Validation Curve for SVM with an RBF kernel
   :srcset: /auto_examples/model_selection/images/sphx_glr_plot_validation_curve_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    import matplotlib.pyplot as plt
    import numpy as np

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

    X, y = load_digits(return_X_y=True)
    subset_mask = np.isin(y, [1, 2])  # binary classification: 1 vs 2
    X, y = X[subset_mask], y[subset_mask]

    disp = ValidationCurveDisplay.from_estimator(
        SVC(),
        X,
        y,
        param_name="gamma",
        param_range=np.logspace(-6, -1, 5),
        score_type="both",
        n_jobs=2,
        score_name="Accuracy",
    )
    disp.ax_.set_title("Validation Curve for SVM with an RBF kernel")
    disp.ax_.set_xlabel(r"gamma (inverse radius of the RBF kernel)")
    disp.ax_.set_ylim(0.0, 1.1)
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.965 seconds)


.. _sphx_glr_download_auto_examples_model_selection_plot_validation_curve.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/model_selection/plot_validation_curve.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_validation_curve.recommendations


.. only:: html

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

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