SVM Kernel Functions

The Support Vector Machine (SVM) algorithm supports two kernel functions: linear and Gaussian. The choice of kernel function depends on the type of model (classification or regression) that you are building and on your data.

SVM Kernel Functions for Classification and Anomaly Detection

For classification and anomaly detection models, use the Gaussian kernel for solving problems where the classes are not linearly separable, that is, the classes cannot be separated by lines or planes. Gaussian kernel models allow for powerful non-linear class separation modeling. If the classes are linearly separable, use the linear kernel.

SVM Kernel Functions for Regression

For regression problems, the linear kernel is similar to approximating the data with a line. The linear kernel is more robust than fitting a line to the data. The Gaussian kernel approximates the data with a non-linear function.