sklearn.svm.NuSVR

A sklearn.svm.NuSVR is a Nu Support Vector Regression System within sklearn.svm module.

• Example(s):
  • Complexity Influence Model [1].
  • …
• 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.OneClassSVM, an Unsupervised Outlier Detection System;
  • sklearn.svm.SVC, a C-Support Vector Classification System;
  • sklearn.svm.SVR, an Epsilon-Support Vector Regression System.
• See: SVM, Classification System, Regularization Task, Ridge Regression Task, Kernel-based Classification Algorithm.

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References

2018a

• (Scikit Learn, 2018B) ⇒ http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVR.html
  • QUOTE: class sklearn.svm.NuSVR(nu=0.5, C=1.0, kernel=’rbf’, degree=3, gamma=’auto’, coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1)source

     Nu Support Vector Regression.

    Similar to NuSVC, for regression, uses a parameter nu to control the number of support vectors. However, unlike NuSVC, where nu replaces C, here nu replaces the parameter epsilon of epsilon-SVR. The implementation is based on libsvm.

2018b

• (Scikit Learn, 2017B) ⇒ http://scikit-learn.org/stable/modules/svm.html#svm-regression Retrieved: 2018-04-10
  • QUOTE: The method of Support Vector Classification can be extended to solve regression problems. This method is called Support Vector Regression.

    The model produced by support vector classification (as described above) depends only on a subset of the training data, because the cost function for building the model does not care about training points that lie beyond the margin. Analogously, the model produced by Support Vector Regression depends only on a subset of the training data, because the cost function for building the model ignores any training data close to the model prediction.

    There are three different implementations of Support Vector Regression: SVR, NuSVR and LinearSVR. LinearSVR provides a faster implementation than SVR but only considers linear kernels, while NuSVR implements a slightly different formulation than SVR and LinearSVR. See Implementation details for further details. (...)

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