Why Support Vector Regression? Difference between SVR and a simple regression model?

• In simple regression, try to minimize the error rate. But in SVR, we try to fit the error within a certain threshold.

Concepts:

1. Boundary

2. Kernel

3. Support Vector

4. Hyper Plane

• Our best fit line is the one where the hyperplane has the maximum number of points.

• We are trying to do here is trying to decide on a decision boundary at e distance from the original hyperplane such that data points closest to the hyperplane or the support vectors are within that boundary line.