Machine Learning - Supervised Learning - Support Vector Machines / Support Vector Regressor Tutorial

Support Vector Machine or SVM is one of the most popular Supervised Learning algorithms, which is used for Classification as well as Regression problems. However, primarily, it is used for Classification problems in Machine Learning.

The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that in the future we can easily predict the correct class for the new data point. This best decision boundary is called a hyperplane.

SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme points are called support vectors, and hence algorithm is termed a Support Vector Machine. Consider the below diagram in which there are two different categories that are classified using a decision boundary or hyperplane:

How does SVM work?

Suppose we want to classify 2D space into two classes. there can be multiple lines between both classes. The algorithm will find the closest point of the line from both classes. These points are called support vectors. The distance between both class vectors with respect to the hyperplane is called as margin.

SVM will select a hyperplane with a maximum margin is called the optimal hyperplane.

Two Types OF SVM-

1] Linear SVM - a dataset that can be classified into two classes by using a single straight line (2-D)

2] Non-Linear SVM - dataset cannot be classified by using a straight line (3-D or more)