Performance comparison of different kernel functions in SVM for different k value in k-fold cross-validation
Ömer Karal · 2020 Innovations in Intelligent Systems and Applications Conference (ASYU) · 2020
It is very important to classify datasets with high accuracy in order to make meaningful decisions in today's science world. In particular, machine learning-based models are known to effectively classify complex datasets. One of the powerful ways to test the success rate of models used for classification is k-fold cross validation. However, very few studies have investigated how k value affects classification results. In this study, the performance of different kernel functions such as Gaussian, linear, and polynomials in SVM were compared for different k values in three different data sets. The most accurate results were obtained with the Gaussian and linear kernel functions.