sklearn.svm.LinearSVC
sklearn.svm.LinearSVC¶ class sklearn.svm.LinearSVC(penalty='l2', loss='squared_hinge', *, dual='warn', tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000)[source]¶ Linear Support Vector Classification. Similar to SVC with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so
NuSVR
NuSVR# class sklearn.svm.NuSVR(*, nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False, max_iter=-1)[source]# Nu Support Vector Regression. Similar to NuSVC, for regression, uses a parameter nu to control the number of support vectors. However, unlike NuSVC, where nu replaces C, here nu replaces the parameter epsilon of epsilon-S
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Plot different SVM classifiers in the iris dataset
