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


=========================================
SVM: Maximum margin separating hyperplane
=========================================

Plot the maximum margin separating hyperplane within a two-class
separable dataset using a Support Vector Machine classifier with
linear kernel.

.. GENERATED FROM PYTHON SOURCE LINES 11-52



.. image-sg:: /auto_examples/svm/images/sphx_glr_plot_separating_hyperplane_001.png
   :alt: plot separating hyperplane
   :srcset: /auto_examples/svm/images/sphx_glr_plot_separating_hyperplane_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


    # Authors: The scikit-learn developers
    # SPDX-License-Identifier: BSD-3-Clause

    import matplotlib.pyplot as plt

    from sklearn import svm
    from sklearn.datasets import make_blobs
    from sklearn.inspection import DecisionBoundaryDisplay

    # we create 40 separable points
    X, y = make_blobs(n_samples=40, centers=2, random_state=6)

    # fit the model, don't regularize for illustration purposes
    clf = svm.SVC(kernel="linear", C=1000)
    clf.fit(X, y)

    plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)

    # plot the decision function
    ax = plt.gca()
    DecisionBoundaryDisplay.from_estimator(
        clf,
        X,
        plot_method="contour",
        colors="k",
        levels=[-1, 0, 1],
        alpha=0.5,
        linestyles=["--", "-", "--"],
        ax=ax,
    )
    # plot support vectors
    ax.scatter(
        clf.support_vectors_[:, 0],
        clf.support_vectors_[:, 1],
        s=100,
        linewidth=1,
        facecolors="none",
        edgecolors="k",
    )
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.059 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_separating_hyperplane.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/svm/plot_separating_hyperplane.ipynb
        :alt: Launch binder
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_separating_hyperplane.recommendations


.. only:: html

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

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