sklearn.svm.NuSVC

A sklearn.svm.LinearSVR is an Nu-Support Vector Classification System within sklearn.svm module.

• Example(s):
  • A Plot different SVM classifiers in the iris dataset [1],
  • A Non-Linear SVM [2]
• Counter-Example(s):
  • sklearn.svm.LinearSVC, a Linear Support Vector Classification System;
  • sklearn.svm.LinearSVR, a Linear Support Vector Regression 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, 2018) ⇒ http://scikit-learn.org/stable/modules/generated/sklearn.svm.NuSVC.html
  • QUOTE: class sklearn.svm.NuSVC(nu=0.5, kernel=’rbf’, degree=3, gamma=’auto’, ...)source

     Nu-Support Vector Classification.

    Similar to SVC but uses a parameter to control the number of support vectors.

    The implementation is based on libsvm.

2018b

• (Scikit Learn, 2017B) ⇒ http://scikit-learn.org/stable/modules/svm.html#svm-classification Retrieved: 2018-04-10
  • QUOTE: SVC, NuSVC and LinearSVC are classes capable of performing multi-class classification on a dataset.

    

     SVC and NuSVC are similar methods, but accept slightly different sets of parameters and have different mathematical formulations (see section Mathematical formulation). On the other hand, LinearSVC is another implementation of Support Vector Classification for the case of a linear kernel. Note that LinearSVC does not accept keyword kernel, as this is assumed to be linear. It also lacks some of the members of SVC and NuSVC, like support. As other classifiers, SVC, NuSVC and LinearSVC take as input two arrays: an array X of size [n_samples, n_features] holding the training samples, and an array y of class labels (strings or integers), size [n_samples] (...)

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