A visual guide to one of the most elegant algorithms in machine learning.

A Support Vector Machine (SVM) is a supervised learning algorithm that finds the optimal boundary between two classes. Unlike algorithms that just find any boundary, SVM specifically hunts for the one with maximum margin — the widest possible "road" between the two groups.

The key insight: a wider margin between classes means better generalisation to new data you haven't seen yet.

Click the canvas to add data points. Watch the SVM recompute the optimal hyperplane in real time.

Each kernel defines a different way of measuring similarity between points.