Maximum Margin Hyperplane

Among all possible hyperplanes that could separate two classes, SVM specifically chooses the Maximum Margin Hyperplane - the one with the largest possible distance to the nearest data points on either side.

What is the Margin

The margin is the distance between the hyperplane and the closest data points from each class. These closest points are called support vectors, and they alone determine the position of the hyperplane.

Why Maximize the Margin

# A larger margin generally means:
# - Better generalization to new, unseen data
# - Lower sensitivity to small changes or noise in the training data

Soft Margin for Overlapping Data

Real-world data is rarely perfectly separable, so SVM uses a "soft margin" that allows some misclassifications, controlled by a regularization parameter (C) that balances margin width against classification errors.

Coming Up Next

Next, you'll learn about the Kernel Trick, which lets SVM handle data that isn't linearly separable.