Kernel Support Vector Machines (KSVMs) are known for their robust performance in supervised learning tasks. However, their computational costs often make them impractical for large datasets. In this study, we investigate two alternatives: the Nyström and Random Fourier Features (RFF) methods, which offer significantly lower computational cost but in their basic form might yield inferior results compared to KSVMs. In this article, we experimentally explore the differences in performance and training time between KSVMs and approximation methods on classification datasets, and the potential of Nyström and RFF ensembles to mitigate the performance gap while maintaining computational efficiency.