.. _example_svm_plot_rbf_parameters.py: ================== RBF SVM parameters ================== This example illustrates the effect of the parameters `gamma` and `C` of the rbf kernel SVM. Intuitively, the `gamma` parameter defines how far the influence of a single training example reaches, with low values meaning 'far' and high values meaning 'close'. The `C` parameter trades off misclassification of training examples against simplicity of the decision surface. A low C makes the decision surface smooth, while a high C aims at classifying all training examples correctly. Two plots are generated. The first is a visualization of the decision function for a variety of parameter values, and the second is a heatmap of the classifier's cross-validation accuracy as a function of `C` and `gamma`. For this example we explore a relatively large grid for illustration purposes. In practice, a logarithmic grid from `10**-3` to `10**3` is usually sufficient. .. rst-class:: horizontal * .. image:: images/plot_rbf_parameters_001.png :scale: 47 * .. image:: images/plot_rbf_parameters_002.png :scale: 47 **Script output**:: The best classifier is: SVC(C=10000.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.001, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) **Python source code:** :download:`plot_rbf_parameters.py ` .. literalinclude:: plot_rbf_parameters.py :lines: 24- **Total running time of the example:** 2.37 seconds ( 0 minutes 2.37 seconds)