Support Vector Machine, Machine Learning, Artificial Intelligence, Data Science, Ambarish 224.
make it difficult to explain the model’s decisions to non-technical stakeholders. In summary, SVMs are powerful algorithm that are widely used in a variety of applications, but they also have some disadvantages, such as sensitivity to kernel choice and the need to tune several hyperparameters.