class sklearn.svm.OneClassSVM(kernel='rbf', degree=3, gamma='auto', coef0=0.0, tol=0.001, nu=0.5, shrinking=True, cache_size=200, verbose=False

sklearn.svm.libsvm.cross_validation() Binding of the cross-validation routine (low-level routine)

sklearn.svm.libsvm.predict() Predict target values of X given a model (low-level method)

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

sklearn.svm.libsvm.fit() Train the model using libsvm (low-level method)

sklearn.svm.l1_min_c(X, y, loss='squared_hinge', fit_intercept=True, intercept_scaling=1.0)

class sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None

sklearn.svm.libsvm.decision_function() Predict margin (libsvm name for this is predict_values) We

class sklearn.svm.NuSVR(nu=0.5, C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, tol=0.001, cache_size=200, verbose=False

class sklearn.svm.NuSVC(nu=0.5, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=0.001, cache_size=200, class_weight=None