UCLA

Support Vector Machine (SVM) is a reliable supervised learning model extensively utilized for classification and regression tasks, owing to its remarkable ability to achieve strong generalization performance. This study focuses on two key factors in the SVM model: the error penalty parameter C and the kernel function. The C parameter is used to balance the model’s complexity and empirical risk, and its selection is crucial for SVM performance. A smaller C value may lead to underfitting, while a larger C can result in overfitting. Additionally, the choice of the kernel function also significantly impacts SVM performance. We will investigate the effects of different kernel functions and parameter settings in the classification task of the Iris dataset and visualize their impacts through a visual approach. The study’s results indicate that, in most cases, the Gaussian kernel outperforms other kernel functions, exhibiting superior classification performance and generalization capability. Therefore, we opt for the Gaussian Radial Basis Function (RBF) kernel and conduct experiments to evaluate the influence of different parameter configurations on classification performance.