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Support Vector Machines

Jun 26, 202611 min read

Support Vector Machines

Imagine you're building a loan approval system. You have historical data on applicants — income and credit score — labeled as default or no default. You train a…

Tutorial

Jun 26, 202611 min read

Hinge Loss and the Dual Problem

Say you've just fit a soft margin SVM. You picked a $C$ value, trained on 8 loan applicants, and got a margin and a set of support vectors. But there's a gap be…

Tutorial

Jun 26, 202611 min read

SVM Kernels

Imagine you're classifying images of handwritten digits. Some digits — like 0 and 8 — have shapes that aren't separable by any straight line in pixel space. Or…

Tutorial

Jun 26, 20269 min read

SVC and SVR

You've learnt the theory — margins, hinge loss, dual problem, kernels. Now it's time to actually run SVM on real data. Theory tells you how SVM should work; pra…

Tutorial

Jun 27, 202614 min read

The Kernel Trick

You fit an SVM to the XOR pattern and it fails — 50% accuracy, no better than guessing. You know the data is not linearly separable, so you add a feature $z = x…