Impact of Kernel Functions on Support Vector Machine Models in Classification and Regression Problems

Support vector machine (SVM) is a powerful machine learning algorithm commonly used in classification and regression problems. The accuracy of the algorithm depends not only on the hyperparameters such as C and epsilon but also on the kernel functions and their parameters. In addition, Grid Search is an effective method that is widely used to determine the optimal hyperparameters and kernel functions of the SVM model. In this study, the effect of different kernel functions on the SVM algorithm is investigated, and the Grid Search procedure is proposed to evaluate the accuracy of these kernel functions. The MNIST data were used for classification problems, and the Boston-housing data were used for regression problems. The results show that applying an appropriate kernel function can significantly improve the prediction accuracy of the SVM algorithm.