Support Vector Machines

Support Vector Machines (SVMs) are powerful supervised learning algorithms used for both classification and regression. SVMs find the optimal decision boundary (hyperplane) that maximises the margin between classes, making them particularly effective for high-dimensional data and problems with clear class separation.

---

The Intuition Behind SVMs

Imagine you have two classes of data points on a 2D plane. There are infinitely many lines that could separate them. An SVM finds the line that maximises the margin — the distance between the decision boundary and the nearest data points from each class.

Key Concepts

Concept	Description
Hyperplane	The decision boundary that separates the classes (a line in 2D, a plane in 3D, a hyperplane in higher dimensions)
Margin	The distance between the hyperplane and the nearest data points from each class
Support Vectors	The data points closest to the hyperplane — they "support" and define the margin
Maximum Margin	The goal of SVM is to find the hyperplane with the largest margin