用于分类的SVM：

class sklearn.svm.SVC(*, C=1.0, kernel='rbf', degree=3, gamma='scale', coef0=0.0, 
shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None, 
verbose=False, max_iter=-1, decision_function_shape='ovr', break_ties=False, 
random_state=None)

https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

用于分类的线性SVM：

class sklearn.svm.LinearSVC(penalty='l2', loss='squared_hinge', *, dual=True, 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)

https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVC.html?highlight=linearsvc#sklearn.svm.LinearSVC

用于回归的SVM：

class sklearn.svm.SVR(*, kernel='rbf', degree=3, gamma='scale', coef0=0.0, tol=0.001, 
C=1.0, epsilon=0.1, shrinking=True, cache_size=200, verbose=False, max_iter=-1)

https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVR.html?highlight=svr#sklearn.svm.SVR

用于回归的线性SVM：

class sklearn.svm.LinearSVR(*, epsilon=0.0, tol=0.0001, C=1.0, loss='epsilon_insensitive',
fit_intercept=True, intercept_scaling=1.0, dual=True, verbose=0, random_state=None, 
max_iter=1000)

https://scikit-learn.org/stable/modules/generated/sklearn.svm.LinearSVR.html?highlight=svr#sklearn.svm.LinearSVR