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

    Click :ref:`here <sphx_glr_download_auto_examples_svm_plot_svm_regression.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_svm_plot_svm_regression.py:


===================================================================
Support Vector Regression (SVR) using linear and non-linear kernels
===================================================================

Toy example of 1D regression using linear, polynomial and RBF kernels.




.. image:: /auto_examples/svm/images/sphx_glr_plot_svm_regression_001.png
    :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none







|


.. code-block:: default

    print(__doc__)

    import numpy as np
    from sklearn.svm import SVR
    import matplotlib.pyplot as plt

    # #############################################################################
    # Generate sample data
    X = np.sort(5 * np.random.rand(40, 1), axis=0)
    y = np.sin(X).ravel()

    # #############################################################################
    # Add noise to targets
    y[::5] += 3 * (0.5 - np.random.rand(8))

    # #############################################################################
    # Fit regression model
    svr_rbf = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1)
    svr_lin = SVR(kernel='linear', C=100, gamma='auto')
    svr_poly = SVR(kernel='poly', C=100, gamma='auto', degree=3, epsilon=.1,
                   coef0=1)
    y_rbf = svr_rbf.fit(X, y).predict(X)
    y_lin = svr_lin.fit(X, y).predict(X)
    y_poly = svr_poly.fit(X, y).predict(X)

    # #############################################################################
    # Look at the results
    lw = 2

    svrs = [svr_rbf, svr_lin, svr_poly]
    kernel_label = ['RBF', 'Linear', 'Polynomial']
    model_color = ['m', 'c', 'g']

    fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)
    for ix, svr in enumerate(svrs):
        axes[ix].plot(X, svr.fit(X, y).predict(X), color=model_color[ix], lw=lw,
                      label='{} model'.format(kernel_label[ix]))
        axes[ix].scatter(X[svr.support_], y[svr.support_], facecolor="none",
                         edgecolor=model_color[ix], s=50,
                         label='{} support vectors'.format(kernel_label[ix]))
        axes[ix].scatter(X[np.setdiff1d(np.arange(len(X)), svr.support_)],
                         y[np.setdiff1d(np.arange(len(X)), svr.support_)],
                         facecolor="none", edgecolor="k", s=50,
                         label='other training data')
        axes[ix].legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),
                        ncol=1, fancybox=True, shadow=True)

    fig.text(0.5, 0.04, 'data', ha='center', va='center')
    fig.text(0.06, 0.5, 'target', ha='center', va='center', rotation='vertical')
    fig.suptitle("Support Vector Regression", fontsize=14)
    plt.show()


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

   **Total running time of the script:** ( 0 minutes  1.319 seconds)


.. _sphx_glr_download_auto_examples_svm_plot_svm_regression.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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