A Comprehensive Guide to Support Vector Machine (SVM) Algorithm

Support Vector Machines (SVM) are a powerful set of supervised learning algorithms used for classification, regression, and outlier detection. In this article, we’ll dive deep into the SVM algorithm, explore its working principles, and provide practical code examples using Python and the Scikit-learn library.

What is a Support Vector Machine (SVM)?

SVM is a supervised learning model primarily used for binary classification tasks. It works by finding the hyperplane that best separates the data points of different classes in a high-dimensional space. The goal of SVM is to maximize the margin between the data points of different classes while ensuring that they are correctly classified.

Key Terminology in SVM:

• Hyperplane: A decision boundary that separates data points of different classes.
• Margin: The distance between the hyperplane and the nearest data point of either class.
• Support Vectors: Data points closest to the hyperplane that influence its position and orientation.
• Kernel Trick: A technique used to transform non-linearly separable data into higher dimensions where it becomes linearly separable.

How Does SVM Work?