How does SVM handle high-dimensional data?

SVM handles high-dimensional data well due to margin maximization and sparsity.

In high-dimensional spaces, SVM remains effective because it focuses only on support vectors and uses regularization to avoid overfitting. Unlike distance-based models, it does not suffer heavily from the curse of dimensionality.