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


==================================================
Plot different SVM classifiers in the iris dataset
==================================================

Comparison of different linear SVM classifiers on a 2D projection of the iris
dataset. We only consider the first 2 features of this dataset:

- Sepal length
- Sepal width

This example shows how to plot the decision surface for four SVM classifiers
with different kernels.

The linear models ``LinearSVC()`` and ``SVC(kernel='linear')`` yield slightly
different decision boundaries. This can be a consequence of the following
differences:

- ``LinearSVC`` minimizes the squared hinge loss while ``SVC`` minimizes the
  regular hinge loss.

- ``LinearSVC`` uses the One-vs-All (also known as One-vs-Rest) multiclass
  reduction while ``SVC`` uses the One-vs-One multiclass reduction.

Both linear models have linear decision boundaries (intersecting hyperplanes)
while the non-linear kernel models (polynomial or Gaussian RBF) have more
flexible non-linear decision boundaries with shapes that depend on the kind of
kernel and its parameters.

.. NOTE:: while plotting the decision function of classifiers for toy 2D
   datasets can help get an intuitive understanding of their respective
   expressive power, be aware that those intuitions don't always generalize to
   more realistic high-dimensional problems.

.. GENERATED FROM PYTHON SOURCE LINES 36-93



.. image-sg:: /auto_examples/svm/images/sphx_glr_plot_iris_svc_001.png
   :alt: SVC with linear kernel, LinearSVC (linear kernel), SVC with RBF kernel, SVC with polynomial (degree 3) kernel
   :srcset: /auto_examples/svm/images/sphx_glr_plot_iris_svc_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 datasets, svm
    from sklearn.inspection import DecisionBoundaryDisplay

    # import some data to play with
    iris = datasets.load_iris()
    # Take the first two features. We could avoid this by using a two-dim dataset
    X = iris.data[:, :2]
    y = iris.target

    # we create an instance of SVM and fit out data. We do not scale our
    # data since we want to plot the support vectors
    C = 1.0  # SVM regularization parameter
    models = (
        svm.SVC(kernel="linear", C=C),
        svm.LinearSVC(C=C, max_iter=10000),
        svm.SVC(kernel="rbf", gamma=0.7, C=C),
        svm.SVC(kernel="poly", degree=3, gamma="auto", C=C),
    )
    models = (clf.fit(X, y) for clf in models)

    # title for the plots
    titles = (
        "SVC with linear kernel",
        "LinearSVC (linear kernel)",
        "SVC with RBF kernel",
        "SVC with polynomial (degree 3) kernel",
    )

    # Set-up 2x2 grid for plotting.
    fig, sub = plt.subplots(2, 2)
    plt.subplots_adjust(wspace=0.4, hspace=0.4)

    X0, X1 = X[:, 0], X[:, 1]

    for clf, title, ax in zip(models, titles, sub.flatten()):
        disp = DecisionBoundaryDisplay.from_estimator(
            clf,
            X,
            response_method="predict",
            cmap=plt.cm.coolwarm,
            alpha=0.8,
            ax=ax,
            xlabel=iris.feature_names[0],
            ylabel=iris.feature_names[1],
        )
        ax.scatter(X0, X1, c=y, cmap=plt.cm.coolwarm, s=20, edgecolors="k")
        ax.set_xticks(())
        ax.set_yticks(())
        ax.set_title(title)

    plt.show()


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

   **Total running time of the script:** (0 minutes 0.195 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_iris_svc.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_iris_svc.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_iris_svc.ipynb
        :alt: Launch JupyterLite
        :width: 150 px

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

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

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

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

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

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


.. include:: plot_iris_svc.recommendations


.. only:: html

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

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