sklearn.svm.LinearSVR

A sklearn.svm.LinearSVR is an Linear Support Vector Regression System within sklearn.svm module.

• Example(s):
  • Support Vector Regression (SVR) using linear and non-linear kernels.
  • …
• Counter-Example(s):
  • sklearn.svm.LinearSVC, a Linear Support Vector Classification System;
  • sklearn.svm.NuSVC, a Nu-Support Vector Classification System;
  • sklearn.svm.NuSVR, a Nu Support Vector Regression 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.LinearSVR.html
  • QUOTE: class sklearn.svm.LinearSVR(epsilon=0.0, tol=0.0001, C=1.0, ...)source

    Linear Support Vector Regression.

    Similar to SVR with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.

    This class supports both dense and sparse input.

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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