SVM is a supervised ML algorithm that attempts to classify data within a dataset by finding the optimal hyperplane that best segregates the classes. Each data point in the dataset can be considered a vector in an N-dimensional plane, with each dimension representing a feature of the data. SVM identifies the frontier data points (or points closest to the opposing class), also known as support vectors, and then attempts to find the boundary (also known as the hyperplane in the N-dimensional space) that is the farthest from the support vector of each class.
Say we have a fruit basket with two types of fruits in it and we want to create an algorithm that segregates them. We only have information about two features of the fruits; that is, their weight and radius. Therefore, we can abstract this problem as a linear algebra problem, with each fruit representing a vector in a two-dimensional space, as shown in the following diagram. In order to segregate the two types of fruit...