What is the SVM algorithm?

[nikhil-waghalkar-46dbf38a (Nikhil Waghalkar)]

SVM stands for Support Vector Machine. It is a supervised machine learning algorithm which is used for classification and regression analysis.

It works with labeled data as it is a part of supervised learning. The goal of support vector machine algorithm is to construct a hyperplane in an N-dimensional space. The hyperplane is a dividing line which distinct the objects of two different classes, it is also known as a decision boundary.

If there are only two distinct classes, then it is called as Binary SVM classifier. A schematic example of binary SVM classifier is given below.

The data point of a class which is nearest to the other class is called a support vector.

There are two types of SVM classifier:

• Linear SVM classifier: A classifier by which we can separate the set of objects into their respective group by drawing a single line, i.e., hyperplane, called as linear SVM classifier.
• Non-Linear SVM classifier: Non-linear SVM classifier applies on those objects which cannot be classified into two groups by a single line.

On the basis of error function, we can divide a SVM model into four categories:

• Classification SVM Type1
• Classification SVM Type2
• Regression SVM Type1
• Regression SVM Type1