# How SVM Works

SVM works by mapping data to a high-dimensional feature space so that data points can be categorized, even when the data are not otherwise linearly separable. A separator between the categories is found, then the data are transformed in such a way that the separator could be drawn as a hyperplane. Following this, characteristics of new data can be used to predict the group to which a new record should belong.

For example, consider the following figure, in which the data points fall into two different categories.

The two categories can be separated with a curve, as shown in the following figure.

After the transformation, the boundary between the two categories can be defined by a hyperplane, as shown in the following figure.

The mathematical function used for the transformation is known as the
**kernel** function. SVM in IBM® SPSS® Modeler
supports the following kernel types:

- Linear
- Polynomial
- Radial basis function (RBF)
- Sigmoid

A linear kernel function is recommended when linear separation of the data is straightforward. In other cases, one of the other functions should be used. You will need to experiment with the different functions to obtain the best model in each case, as they each use different algorithms and parameters.