"What is support vector machine algorithm?"

Support vector machine algorithm:

A supervised machine learning approach called Support Vector Machine (SVM) is used for both classification and regression. Although we also refer to regression concerns, categorization is the most appropriate term. Finding a hyperplane in an N-dimensional space that clearly classifies the data points is the goal of the SVM method.

 Support vector in SVM algorithm:

 Support vectors are data points that are closer to the hyperplane and have an impact on the hyperplane's position and orientation. By utilizing these support vectors, we increase the classifier's margin. The hyperplane's location will vary if the support vectors are deleted. These are the ideas that aid in the development of our SVM.

Where SVM algorithm is used?

SVMs are utilized in web pages, intrusion detection, face identification, email categorization, gene classification, and handwriting recognition, among other applications. We utilise SVMs in machine learning for a number of reasons, including this. Both classification and regression on linear and non-linear data are supported.

Why is it called a support vector?

They are known as support vectors because if they move, the hyperplane will follow. This indicates that the hyperplane is independent of all other observations and is only dependent on the support vectors. SVM, which we have only discussed so far, can only categorize data that can be separated linearly.

 Types of SVM algorithms:

Support vector machines are broadly classified into two types: 

1. Simple or linear SVM 

2. Kernel or non-linear SVM.