.. 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_unbalanced.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_unbalanced.py>`
        to download the full example code. or to run this example in your browser via JupyterLite or Binder

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

.. _sphx_glr_auto_examples_svm_plot_separating_hyperplane_unbalanced.py:


=================================================
SVM: Separating hyperplane for unbalanced classes
=================================================

Find the optimal separating hyperplane using an SVC for classes that
are unbalanced.

We first find the separating plane with a plain SVC and then plot
(dashed) the separating hyperplane with automatically correction for
unbalanced classes.

.. currentmodule:: sklearn.linear_model

.. note::

    This example will also work by replacing ``SVC(kernel="linear")``
    with ``SGDClassifier(loss="hinge")``. Setting the ``loss`` parameter
    of the :class:`SGDClassifier` equal to ``hinge`` will yield behaviour
    such as that of a SVC with a linear kernel.

    For example try instead of the ``SVC``::

        clf = SGDClassifier(n_iter=100, alpha=0.01)

.. GENERATED FROM PYTHON SOURCE LINES 27-96



.. image-sg:: /auto_examples/svm/images/sphx_glr_plot_separating_hyperplane_unbalanced_001.png
   :alt: plot separating hyperplane unbalanced
   :srcset: /auto_examples/svm/images/sphx_glr_plot_separating_hyperplane_unbalanced_001.png
   :class: sphx-glr-single-img





.. code-block:: Python


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

    import matplotlib.lines as mlines
    import matplotlib.pyplot as plt

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

    # we create two clusters of random points
    n_samples_1 = 1000
    n_samples_2 = 100
    centers = [[0.0, 0.0], [2.0, 2.0]]
    clusters_std = [1.5, 0.5]
    X, y = make_blobs(
        n_samples=[n_samples_1, n_samples_2],
        centers=centers,
        cluster_std=clusters_std,
        random_state=0,
        shuffle=False,
    )

    # fit the model and get the separating hyperplane
    clf = svm.SVC(kernel="linear", C=1.0)
    clf.fit(X, y)

    # fit the model and get the separating hyperplane using weighted classes
    wclf = svm.SVC(kernel="linear", class_weight={1: 10})
    wclf.fit(X, y)

    # plot the samples
    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired, edgecolors="k")

    # plot the decision functions for both classifiers
    ax = plt.gca()
    disp = DecisionBoundaryDisplay.from_estimator(
        clf,
        X,
        plot_method="contour",
        colors="k",
        levels=[0],
        alpha=0.5,
        linestyles=["-"],
        ax=ax,
    )

    # plot decision boundary and margins for weighted classes
    wdisp = DecisionBoundaryDisplay.from_estimator(
        wclf,
        X,
        plot_method="contour",
        colors="r",
        levels=[0],
        alpha=0.5,
        linestyles=["-"],
        ax=ax,
    )

    plt.legend(
        [
            mlines.Line2D([], [], color="k", label="non weighted"),
            mlines.Line2D([], [], color="r", label="weighted"),
        ],
        ["non weighted", "weighted"],
        loc="upper right",
    )
    plt.show()


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

   **Total running time of the script:** (0 minutes 0.159 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_separating_hyperplane_unbalanced.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/1.6.X?urlpath=lab/tree/notebooks/auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb
        :alt: Launch binder
        :width: 150 px

    .. container:: lite-badge

      .. image:: images/jupyterlite_badge_logo.svg
        :target: ../../lite/lab/index.html?path=auto_examples/svm/plot_separating_hyperplane_unbalanced.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_separating_hyperplane_unbalanced.recommendations


.. only:: html

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

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