Let us understand the working of SVM by taking an example where we have two classes that are shown is the below image which are a class A: Circle & class B: Triangle. There can be many hyperplanes that can do this task but the objective is to find that hyperplane that has the highest margin that means maximum distances between the two classes, so that in future if a new data point comes that is two be classified then it can be classified easily. The main function of the SVM is to check for that hyperplane that is able to distinguish between the two classes.
We will explain in this blog What is SVM, how SVM works, pros and cons of SVM, and hands on problem using SVM in python.Ī support vector machine is a machine learning model that is able to generalise between two different classes if the set of labelled data is provided in the training set to the algorithm. It is good because it gives reliable results even if there is less data. It is preferred over other classification algorithms because it uses less computation and gives notable accuracy. If you have not followed the same algorithm I would recommend you to go through them first before moving to support vector machines.Ī support vector machine is a very important and versatile machine learning algorithm, it is capable of doing linear and nonlinear classification, regression and outlier detection. Support vector machines also known as SVM is another algorithm widely used by machine learning people for both classification as well as regression problems but is widely used for classification tasks. I assume that by now would have been familiar with linear regression and logistic regression algorithms.