sklearn.svm.SVC

A sklearn.svm.SVC is a C-Support Vector Classification System within sklearn.svm module.

• Example(s):

** a SVM Exercise

• 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.OneClassSVM, an Unsupervised Outlier Detection 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.SVC.html
  • QUOTE: class sklearn.svm.SVC(C=1.0, kernel=’rbf’, degree=3, gamma=’auto’, coef0=0.0, ...)source

     C-Support Vector Classification.

    The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than a couple of 10000 samples.

    The multiclass support is handled according to a one-vs-one scheme.

    For details on the precise mathematical formulation of the provided kernel functions and how gamma, coef0 and degree affect each other, see the corresponding section in the narrative documentation: Kernel functions.

2018b

• (Scikit Learn, 2017B) ⇒ http://scikit-learn.org/stable/modules/svm.html#svm-classificationt 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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