SVM Hyperparameters

The main hyperparameters of an SVM are:

• Regularization parameter ($C$): Controls the trade-off between maximizing the margin and minimizing misclassifications. A large \(C\) attempts to classify all training examples correctly (risk of overfitting), while a small \(C\) allows more violations (better generalization).

• Kernel function: Self-explanatory.

• Kernel-specific parameters: Depends on the selected kernel.