Support Vector Regressor
Support Vector Machines (SVMs) are well known in classification problems. However, the use of SVMs in regression is not as well documented. These types of models are known as Support Vector Regression (SVR).
SVR gives us the flexibility to define how much error is acceptable in our model and will find an appropriate line to fit the data.
Kernels
- SVM algorithms use a set of mathematical functions, defined as the kernel.
- The function of the kernel is to take data as input and transform it into the required form.
- Different SVM algorithms use different types of kernel functions. These functions can be different types.
- For example, linear, nonlinear, polynomial, radial basis function (RBF), and sigmoid.
- The most used type of kernel function is RBF. It is also the default kernel.
Support Vector Regression Implementation
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