Supervised Learning—Classification Using Support Vector Machines
Wei-Meng Lee · 2019
This chapter helps the coders to learn a supervised machine learning algorithm that is very popular among data scientists—Support Vector Machines (SVM). Like logistics regression, SVM is also a classification algorithm. The main idea behind SVM is to draw a line between two or more classes in the best possible manner. Once the line is drawn to separate the classes, one can then use it to predict future data. The chapter helps the coders to learn how SVM works and the various techniques the coders can use to adapt SVM for solving nonlinearly-separable datasets. A key term in SVM is support vectors. Support vectors are the points that lie on the two margins. With the series of points, the next question would be to find the formula for the hyperplane, together with the two margins. The chapter presents an example to see how SVM works and how to implement it using Scikit-learn.