Support Vector Machine Explained

A Support Vector Machine, or SVM, finds the best boundary that separates data points belonging to different categories.

Rather than just drawing any line between classes, SVM finds the one that leaves the widest possible gap between the closest points of each category.

For example, when separating spam and non-spam emails based on word patterns, SVM tries to draw a boundary that keeps both groups as far apart as possible, reducing the chance of misclassification.

• The closest data points to the boundary are called support vectors, and they define its position.
• SVM works well even when the number of features is very high.
• It can handle both simple straight-line boundaries and more complex, curved ones.

SVM is especially valuable when a clear separation between categories genuinely exists in the data.