Support Vector Regression
SVR is based on the same principles as the Support Vector Machine (SVM). In fact, SVR is the adapted form of SVM when the dependent variable is numeric rather than categorical. One of the main advantages of using SVR is that it is a nonparametric technique.
To build the model, the SVR technique uses the kernel functions. The commonly used kernel functions are:
- Linear
- Polynomial
- Sigmoid
- Radial base
This technique allows the fitting of a nonlinear model without changing the explanatory variables, helping to interpret the resulting pattern better.
In the SVR, we do not have to worry about the prediction as long as the error (ε) remains above a certain value. This method is called the maximal margin principle. The maximal margin allows SVR to be seen as a convex optimization problem.
Regression can also be penalized using a cost parameter, which becomes useful in avoiding excess adaptation. SVR is a useful technique that provides the user with a great flexibility in distributing...
