Example: RBF SVM parameters

RBF SVM parameters

This example illustrates the effect of the parameters gamma and C of the Radial Basis Function (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 gamma parameters can be seen as the inverse of the radius of influence of samples selected by the model as support vectors.

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 by giving the model freedom to select more samples as support vectors.

The first plot is a visualization of the decision function for a variety of parame