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 http://contrib.scikit-learn.org/imbalanced-learn/about.html1.
The Modeling tab on the palette contains the One-Class SVM node and other Python nodes.
1Smola, 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)