# One-Class SVM node

The One-Class SVM© node uses an unsupervised learning algorithm. The node can be used for novelty detection. It will detect the soft boundary of a given set of samples, to then classify new points as belonging to that set or not. This One-Class SVM modeling node is implemented in Python and requires the scikit-learn© Python library.

For details about the scikit-learn library, see Support Vector Machines^{1}.

The Modeling tab on the palette contains the One-Class SVM node and other Python nodes.

Note: One-Class SVM is used for usupervised outlier and novelty detection. In
most cases, we recommend using a known, "normal" dataset to build the model so the algorithm can set
a correct boundary for the given samples. Parameters for the model – such as nu, gamma, and kernel –
impact the result significantly. So you may need to experiment with these options until you find the
optimal settings for your situation.

^{1}Smola, Schölkopf. "A Tutorial on Support Vector Regression."
*Statistics and Computing Archive*, vol. 14, no. 3, August 2004, pp. 199-222.
(http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.114.4288)