Support Vector Machines (SVM): An Intuitive Explanation






Support Vector Machines (SVMs) are a type of supervised machine learning algorithm used for classification and regression tasks. They are widely used in various fields, including pattern recognition, image analysis, and natural language processing.

A hyperplane is a decision boundary that separates data points into different classes in a high-dimensional space. In two-dimensional space, a hyperplane is simply a line that separates the data points into two classes. In three-dimensional space, a hyperplane is a plane that separates the data points into two classes. Similarly, in N-dimensional space, a hyperplane has (N-1)-dimensions.

A margin is the distance between the decision boundary (hyperplane) and the closest data points from each class. The goal of SVMs is to maximize this margin while minimizing classification errors. A larger margin indicates a greater degree of confidence in the classification, as it means that there is a larger gap between the decision boundary and the closest data points from each class. The margin is a measure of how well-separated the classes are in feature space. SVMs are designed to find the hyperplane that maximizes this margin, which is why they are sometimes referred to as maximum-margin classifiers.