Regression in Machine Learning.

Support Vector Regression(SVR) SVR is a powerful algorithm that allows us to choose how tolerant we are of errors, both through an acceptable error margin(ϵ) and through tuning our tolerance of falling outside that acceptable error rate. Instead of a simple line, it takes a tube of width epsilon(ϵ) which is an intensive tube. Here, the first part of the formula is used to minimize the coefficients whereas the second part of the formula is responsible for tuning the epsilon(ϵ). The graph on the left represents the regression fit line on linear regression models and the graph on the right represents the regression fit line on SVR. The points outside the Intensive Tube(ϵ) are knowns as support vectors which dictate the position of the Intensive Tube(ϵ).