We have already seen some examples in which we use a straight line to separate classes.
As the dimensionality, or feature space, of a model increases, there may be many different ways to separate classes, in both linear and non-linear ways.
In the cases of support vector machines, data is first transformed into a higher dimensional space using a mapping function known as a kernel, and an optimal hyperplane is used to segment the higher dimensional space. A hyperplane uses one dimension less than the space it is trying to measure, so a straight line is used to segment a two-dimensional space, and a 2-dimensional sheet of paper is used to segment a three-dimensional space. The hyperplane can be either linear or non-linear.
Hyperplanes use support vectors which are important training tuples and are used to define the boundaries of each class. They are the most critical points in the data, and they are the most important points used which support the definition of the hyperplane...