As we previously described, SVMs can be used to perform linear classification over two distinct classes. An SVM will attempt to find a hyperplane that separates the two classes such that the estimated hyperplane describes the maximum achievable margin of separation between the two classes in our model.

For example, an estimated hyperplane between two classes of data can be visualized using the following plot diagram:

As depicted in the graph shown in the preceding plot diagram, the circles and crosses are used to represent the two classes of input values in the sample data. The line represents the estimated hyperplane ...

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