Which classifier seeks to maximize the margin by identifying the optimal boundary?

Which classifier seeks to maximize the margin by identifying the optimal boundary?

• A. SVMs
• B. Maximum Margin Classifier
• C. Random Forests
• D. AdaBoost

Answer: (B)

The idea being tested is that choosing a boundary with the largest possible distance to the nearest points from either class yields a more robust, generalizable classifier. This is exactly what the maximum margin classifier does: it finds the decision boundary that maximizes the margin, the gap between the boundary and the closest data points (the support vectors) from each class. In practice, this is the core objective of a support vector machine, which tunes the boundary to push it as far away as possible from the training samples while still separating them. That’s why this option is the best fit. The other methods don’t optimize this geometric margin: Random Forests build many trees and aggregate, and AdaBoost focuses on hard cases by reweighting samples rather than maximizing a margin.

The idea being tested is that choosing a boundary with the largest possible distance to the nearest points from either class yields a more robust, generalizable classifier. This is exactly what the maximum margin classifier does: it finds the decision boundary that maximizes the margin, the gap between the boundary and the closest data points (the support vectors) from each class. In practice, this is the core objective of a support vector machine, which tunes the boundary to push it as far away as possible from the training samples while still separating them. That’s why this option is the best fit. The other methods don’t optimize this geometric margin: Random Forests build many trees and aggregate, and AdaBoost focuses on hard cases by reweighting samples rather than maximizing a margin.