What is the impact of feature correlation on SVM performance?

Highly correlated features can reduce SVM efficiency but do not break the model.

SVM can handle correlated features because it relies on optimization rather than probabilistic independence. However, redundancy increases dimensional noise and may lead to unstable weight vectors in linear SVM and slower convergence in kernel SVM.