Why is SVM sensitive to feature scaling?

SVM relies on distance calculations, making it sensitive to feature magnitude differences.

Features with larger scales dominate the distance computation, skewing the hyperplane. Scaling ensures each feature contributes equally to kernel and dot product computations.