Another supervised learning algorithm we present here is the Support Vector Machine (SVM). Unlike MLR which minimizes the prediction error, SVM aims to find the optimal hyperplane which maximizes the margin between the vectors of two classes in a binary classification problem . Specifically, fitting a SVM involves solving the following optimization problem for all 𝑖 = 1, 2, … , 𝑁: