SVM Kernels & Margin - Interactive Visualization

A Support Vector Machine finds the hyperplane that maximizes the margin between two classes - the widest possible road separating positive from negative examples. When classes are not linearly separable, kernel functions implicitly map data to a higher-dimensional space where a linear boundary does exist. The kernel trick lets SVMs find complex boundaries without ever computing high-dimensional coordinates explicitly.

• See the maximum-margin hyperplane and the support vectors that define it - only a few points matter
• Switch between Linear, RBF, and Polynomial kernels and watch the boundary shape transform
• Adjust the regularization parameter C to trade off between margin width and training error
• Understand soft-margin SVM: allowing some misclassifications to get a wider, more robust margin
• Learn why the kernel trick is computationally elegant - it computes dot products in feature space without explicit transformation

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.