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Support Vector Machine Q&A
1What is SVM?
Answer: Margin-based classifier finding optimal separating hyperplane.
2What are support vectors?
Answer: Boundary-near points that define the decision margin.
3What is margin?
Answer: Distance between hyperplane and closest class points.
4Hard vs soft margin?
Answer: Hard allows no errors; soft allows some via regularization C.
5Kernel trick?
Answer: Implicitly maps data to higher-dimensional space for nonlinear separation.
6Common kernels?
Answer: Linear, polynomial, RBF, sigmoid.
7Role of C parameter?
Answer: Controls misclassification penalty and margin flexibility.
8Role of gamma in RBF?
Answer: Controls influence radius of each training point.
9Need feature scaling?
Answer: Yes, SVM is sensitive to feature scale.
10SVM pros?
Answer: Effective in high-dimensional settings with clear margins.
11SVM limitations?
Answer: Slower on very large datasets and sensitive to hyperparameters.
12One-line summary?
Answer: SVM is a powerful margin-based model for well-separated classes.