The supervised learning algorithm Support Vector Regression is used to predict discrete values. SVMs and Support Vector Regression are both based on the same premise. SVR's main premise is to locate the best-fitting line. The best fit line in SVR is the hyperplane with the greatest number of points.
Support Vector Machine can also be utilised as a regression approach while retaining all of the algorithm's fundamental characteristics (maximal margin). With a few minor exceptions, the Support Vector Regression (SVR) uses the same principles as the SVM for classification.
One of the key advantages of SVR is that its computational complexity is independent of the input space's dimensionality. It also has a high prediction accuracy and great generalisation capabilities. The purpose of this chapter is to give an overview of SVR and Bayesian regression.