sklearn.svm.OneClassSVM

• • A sklearn.svm.OneClassSVM is an Unsupervised Outlier Detection System based on SVM within sklearn.svm module.
• Example(s):
  • An One-class SVM with non-linear kernel (RBF)
• Counter-Example(s):
  • sklearn.svm.LinearSVC, a Linear Support Vector Classification System;
  • sklearn.svm.LinearSVR, a Linear Support Vector Regression System;
  • sklearn.svm.NuSVC, a Nu-Support Vector Classification System;
  • sklearn.svm.NuSVR, a Nu Support Vector Regression System;
  • sklearn.svm.SVC, a C-Support Vector Classification System;
  • sklearn.svm.SVR, an Epsilon-Support Vector Regression System.
• See: Decision Tree, Classification System, Regularization Task, Ridge Regression Task, Kernel-based Classification Algorithm.

---

---

References

2018a

• (Scikit Learn, 2018) ⇒ http://scikit-learn.org/stable/modules/generated/sklearn.svm.OneClassSVM.html
  • QUOTE: class sklearn.svm.OneClassSVM(kernel=’rbf’, degree=3, gamma=’auto’, coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False, max_iter=-1, random_state=None)source

     Unsupervised Outlier Detection.

    Estimate the support of a high-dimensional distribution.

    The implementation is based on libsvm.

2018b

• (Scikit Learn, 2017B) ⇒ http://scikit-learn.org/stable/modules/svm.html#svm-outlier-detection Retrieved: 2018-04-10
  • QUOTE: One-class SVM is used for novelty detection, that is, given a set of samples, it will detect the soft boundary of that set so as to classify new points as belonging to that set or not. The class that implements this is called OneClassSVM.

    In this case, as it is a type of unsupervised learning, the fit method will only take as input an array X, as there are no class labels.

    See, section Novelty and Outlier Detection for more details on this usage.