Support Vector Machines

Key concepts:

• Hard vs. soft margins, C parameter.
• Kernel trick: RBF, polynomial; feature space intuition.
• Class imbalance, probability calibration.

Common interview checks:

• Choosing C and gamma; scaling requirements.
• Pros/cons vs. logistic regression and trees.