Learn how SVMs are used for classification and regression problems, and how they find a hyperplane that maximizes the margin between classes. See examples of SVM implementation in Python using sklearn library and kernel trick. Support Vector Machine (SVM) is a supervised machine learning algorithm used for classification and regression tasks. It tries to find the best boundary known as hyperplane that separates different classes in the data. 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.
