RBF kernel: it is mainly used in the case of linear indivisibility. There are many parameters, and the classification results are very dependent on the parameters. Many people find the appropriate parameters through the cross-validation of training data, but this process is time-consuming. If the number of features is small and the number of samples in general, which is neither large nor small, the SVM with RBF kernel is selected.