Despite their advantages, SVMs also have some limitations:
Computationally intensive: Training SVMs, especially with nonlinear kernels, can be time-consuming. Choice of kernel: The performance of SVMs heavily depends on the choice of kernel and its parameters, which may require extensive hyperparameter tuning. Interpretability: SVMs are often considered as black-box models, making it difficult to interpret the relationships they capture.
