SVM-RBF核下的参数调参准则

When training an SVM with the Radial Basis Function (RBF) kernel, two parameters must be considered: C and gamma. The parameter C, common to all SVM kernels, 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. gamma defines how much influence a single training example has. The larger gamma is, the closer other examples must be to be affected.Proper choice of C and gamma is critical to the SVM’s performance. One is advised to use sklearn.model_selection.GridSearchCV with C and gamma spaced exponentially far apart to choose good values.