Svm in machine learning: Learn how to use support vector machines

Learn how to use support vector machines (SVMs) for classification, regression and outliers detection in scikit-learn. Find out the advantages, disadvantages, parameters and examples of SVMs with different kernels and multi-class strategies. Support Vector Machine (SVM) is a supervised learning algorithm used for classification and regression. It finds the optimal boundary to separate classes, ensuring maximum margin. This article explores SVM’s working, mathematical foundation, types, real-world applications, and implementation with examples. A support vector machine ( SVM ) is a supervised machine learning algorithm that classifies data by finding an optimal line or hyperplane that maximizes the distance between each class in an N-dimensional space. Definition ‘Support Vector Machine is a system for efficiently training linear learning machines in kernel-induced feature spaces, while respecting the insights of generalisation theory and exploiting optimisation theory.’ AN INTRODUCTION TO SUPPORT VECTOR MACHINES (and other kernel-based learning methods) N. Cristianini and J. Shawe-Taylor Cambridge University Press