How a Linear SVM Finds the Best Boundary

This animation shows a linear support vector machine separating two classes of points with a straight decision boundary. It highlights the margin, the support vectors that define it, and how the algorithm maximizes the distance between classes for the most robust separation. Useful for students learning classification methods in machine learning, and for teachers introducing optimization and geometry concepts behind supervised learning models.

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

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linear svm

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• Show how soft margins allow misclassified points in a non-separable dataset
• Animate the kernel trick transforming data into higher dimensions for nonlinear SVMs
• Compare decision boundaries of SVM versus logistic regression on the same dataset

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