sklearn.svm.LinearSVC

A sklearn.svm.LinearSVC is an Linear Support Vector Classification System within sklearn.svm module.

• Example(s):
  • Comparison of Calibration of Classifiers;
  • Plot different SVM classifiers in the iris dataset;
  • Scaling the regularization parameter for SVCs.
  • …
• Counter-Example(s):
  • 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.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, 2017B) ⇒ http://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html
  • QUOTE: class sklearn.svm.LinearSVC(penalty=’l2’, ...)source

    Linear Support Vector Classification.

    Similar to SVC 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 and the multiclass support is handled according to a one-vs-the-rest scheme.

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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