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# Baby Steps : Support Vector Regression in Python | by Tharun Peddisetty | Aug, 2020

Support vector regression is a special kind of regression that gives you some sort of buffer or flexibility with the error. How does it do that ? I’m going to explain it to you in simple terms by showing 2 different graphs.

The above is an hypothetical linear regression graph. You can see that the regression line is drawn at a position with minimum sqaured errors. Errors are basically the sqaures of difference in distance between the original data point (points in black) and the regression line (predicted values).

The above is the same setting with SVR(Support Vector Regression). You can observe that there are 2 boundaries around the regression line. This is a tube with the vertical distance of epsilon above and below the regression line. In reality, it is kown as epsilon insensitive tube. The role of this tube is that it creates a buffer for the error. To be specific, all the data points within this tube are considered to have zero error from the regression line. Only the points outside of this tube are considered for calculating the errors. The error is calculated as the distance from the data point to the boundary of the tube rather than data point to the regression line (as seen in Linear Regression)

Why support vector ?

Well, all the points outside of the tube are known as slack points and they are essentially vectors in a 2-dimensional space. Imagine drawing vectors from the origin to the individual slack points, then you can see all the vectors in the graph. These vectors are supporting the structure or formation of the this tube and hence it is known as support vector regression. You can understand it from the below graph.