.. _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
    :align: center





.. code-block:: python

    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=1e3, gamma=0.1)
    svr_lin = SVR(kernel='linear', C=1e3)
    svr_poly = SVR(kernel='poly', C=1e3, degree=2)
    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
    plt.scatter(X, y, color='darkorange', label='data')
    plt.plot(X, y_rbf, color='navy', lw=lw, label='RBF model')
    plt.plot(X, y_lin, color='c', lw=lw, label='Linear model')
    plt.plot(X, y_poly, color='cornflowerblue', lw=lw, label='Polynomial model')
    plt.xlabel('data')
    plt.ylabel('target')
    plt.title('Support Vector Regression')
    plt.legend()
    plt.show()

**Total running time of the script:** ( 0 minutes  0.967 seconds)



.. container:: sphx-glr-footer


  .. 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>`

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

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