As you have probably noticed by now, there are several machine learning algorithms available at your disposal. In my previous post, I covered a very popular classification algorithm called K-Nearest Neighbors. In today’s post, I will cover another very common and powerful classification algorithm called Support Vector Machine (SVM).
What is SVM and how does it work?
Just like KNN, SVM is a supervised learning model which means that it learns from the training set that we feed it. It can be used for both classification and regression problems but it’s mostly used for classification. In this post, we will focus on using SVM for classification.
SVM consists of picking support vectors and then using them to define a decision boundary for classifying features into different classes. The decision boundary is more formally known as hyperplane. Points on different sides of the plane belong to different classes. However, different sets of points can be segregated by numerous hyperplanes so how do you decide which hyperplane to select? That’s where the support vectors come into the picture.