Support Vector Machine (SVM)

Overview

One of the most common tasks in machine learning is separating information into categories.

For example, an email may need to be classified as spam or not spam. A financial transaction may need to be classified as legitimate or fraudulent.

Support Vector Machines, often called SVMs, were designed to solve this type of problem.

Rather than simply separating categories, an SVM tries to find the clearest possible boundary between them.

Imagine drawing a line between two groups of points on a graph. There may be many possible lines, but some create a larger gap between the groups than others. SVM searches for the boundary that maximizes that separation.

The larger the separation, the more confident the model can be when classifying new examples.

Although newer machine learning approaches have become popular in some applications, SVMs remain an important foundational concept in machine learning education.

Why It Matters

Support Vector Machines help organizations classify information accurately, especially when distinguishing between categories is important.

Real-World Example

An email provider may use an SVM model to help distinguish between spam messages and legitimate emails.

Related Concepts

• Classification
• Machine Learning
• Model Training
• Prediction
• Data Points