The SVM method is a set of algorithms used for classification and regression analysis tasks. Considering that in an N-dimensional space, each object belongs to one of two classes, SVM generates an (N-1)-dimensional hyperplane to divide these points into two groups. It's similar to an on-paper depiction of points of two different types that can be linearly divided. Furthermore, the SVM selects the hyperplane, which is characterized by the maximum distance from the nearest group elements.
The input data can be separated using various hyperplanes. The best hyperplane is a hyperplane with the maximum resulting separation and the maximum resulting difference between the two classes.
Imagine the data points on the plane. In the following case, ...
