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Practical Predictive Analytics by Ralph Winters

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Support vector machines

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. ...

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