SUPPORT VECTOR REGRESSION ( SVM )

INTRODUCTION & INTUTION

1. SVR performs linear regression in higher dimensions
2. In simple regression we try to minimise the error rate. While in SVR we try to fit the error within a certain threshold
3. In Linear regession we try to minimize the error between the predicted and data. In SVR our goal is to make sure that errors do not exceed the threshold
4. Advantage of SVR over OLSE linear regression is SVR can minimize overfitting problem.