What is the difference between LinearSVC and SVC(kernel="linear")?

user_562769 0
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I found sklearn.svm.LinearSVC and sklearn.svm.SVC(kernel='linear') and they seem very similar to me, but I get very different results on Reuters.

sklearn.svm.LinearSVC: 81.05% in   28.87s train /    9.71s test
sklearn.svm.SVC      : 33.55% in 6536.53s train / 2418.62s test

Both have a linear kernel. The tolerance of the LinearSVC is higher than the one of SVC:

LinearSVC(C=1.0, tol=0.0001, max_iter=1000, penalty='l2', loss='squared_hinge', dual=True, multi_class='ovr', fit_intercept=True, intercept_scaling=1)
SVC      (C=1.0, tol=0.001,    max_iter=-1, shrinking=True, probability=False, cache_size=200, decision_function_shape=None)

How do both functions differ otherwise? Even if I set kernel='linear, tol=0.0001, max_iter=1000 anddecision_function_shape='ovr'theSVCtakes much longer thanLinearSVC`. Why?

I use sklearn 0.18 and both are wrapped in the OneVsRestClassifier. I'm not sure if this makes the same as multi_class='ovr' / decision_function_shape='ovr'.

scikit-learn svm
2 Answers

The main difference between them is linearsvc lets your choose only linear classifier whereas svc let yo choose from a variety of non-linear classifiers. however it is not recommended to use svc for non-linear problems as they are super slow. try importing other libraries for doing non-linear classifications.

now the point that even after defining kernel='linear' we don't get same output is because both linearsvc and svc try different approaches while doing the background mathematics. also linearsvc works on principle of one-vs-rest, and svc works on one-vs-one.

I hope this answers your question.

user_159140580
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