Support Vector Machine Classification with Pandas, Scikit-Learn, and PySpark

Abstract

This chapter explores support vector machines (SVMs), widely employed supervised learning algorithms recognized for their effectiveness in binary classification tasks. SVMs aim to find an optimal hyperplane (a decision plane that separates objects with different class memberships) that maximizes the margin between data points of different classes. The hyperplane acts as a decision boundary, with one class on each side. The margin represents the perpendicular distance between the hyperplane and the closest points of each class. A larger margin indicates a better separation, while a smaller margin suggests a less optimal decision boundary.