Support Vector Machines

SVMs take a different approach: instead of averaging many models, find the single best boundary between classes by maximising the margin. This lecture covers the maximum-margin idea, soft margins (the C parameter), and the kernel trick for nonlinear boundaries. We compare SVMs with random forests and discuss when each approach is more appropriate.