Support Vector Machines (SVM)
Support Vector Machines (SVMs) Support Vector Machines (SVMs) are a robust and versatile set of algorithms utilized in machine learning and data science for classification and regression tasks. Originally developed for binary classification by Cortes & Vapnik in 1995, SVMs are distinguished by their capability to find an optimal hyperplane that categorizes observations into distinct classes. This guide delves into the theoretical underpinnings, operational mechanics, and practical applications of SVMs....