How a Linear SVM Finds the Best Boundary

This animation shows how a linear support vector machine separates two classes of points with a straight decision boundary. It highlights the margin, the support vectors that define it, and why maximizing the margin gives better generalization than any arbitrary separating line. Viewers see the optimization process adjusting the boundary until it is equidistant from the closest points of each class. Useful for students and teachers introducing classification, margins, and the geometric intuition behind support vector machines.

Narrated · 16:9 · every frame verified for overlaps, spacing and edges before rendering

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• Show how soft margins handle overlapping or non-separable data with slack variables
• Explain the kernel trick and how SVMs classify nonlinear boundaries
• Compare SVM decision boundaries to logistic regression on the same dataset

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