SVC vs LinearSVC in scikit learn: difference of loss function

Question

According to this post, SVC and LinearSVC in scikit learn are very different. But when reading the official scikit learn documentation, it is not that clear.

Especially for the loss functions, it seems that there is an equivalence:

And this post says that le loss functions are different:

• SVC : 1/2||w||^2 + C SUM xi_i
• LinearSVC: 1/2||[w b]||^2 + C SUM xi_i

It seems that in the case of LinearSVC, the intercept is regularized, but the official documentation says otherwise.

Does anyone have more information? Thank you

Answer 1

SVC is a wrapper of LIBSVM library, while LinearSVC is a wrapper of LIBLINEAR

LinearSVC is generally faster then SVC and can work with much larger datasets, but it can only use linear kernel, hence its name. So the difference lies not in the formulation but in the implementation approach.

Quoting LIBLINEAR FAQ:

When to use LIBLINEAR but not LIBSVM

There are some large data for which with/without nonlinear mappings gives similar performances. 
Without using kernels, one can quickly train a much larger set via a linear classifier. 
Document classification is one such application. 
In the following example (20,242 instances and 47,236 features; available on LIBSVM data sets), 
the cross-validation time is significantly reduced by using LIBLINEAR:

% time libsvm-2.85/svm-train -c 4 -t 0 -e 0.1 -m 800 -v 5 rcv1_train.binary
Cross Validation Accuracy = 96.8136%
345.569s

% time liblinear-1.21/train -c 4 -e 0.1 -v 5 rcv1_train.binary
Cross Validation Accuracy = 97.0161%
2.944s

Warning:While LIBLINEAR's default solver is very fast for document classification, it may be slow in other situations. See Appendix C of our SVM guide about using other solvers in LIBLINEAR.
Warning:If you are a beginner and your data sets are not large, you should consider LIBSVM first.