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,verbose=False,max_iter=-1,decision_function_shape=None,random_state=None) 1. """ sklearn.svm.SVC(C=1.0, kernel='rbf', degree=3, g...
"RandomForest": RandomForestClassifier(n_estimators=100), "SVM": SVC(probability=True, kernel='rbf'), "NeuralNet": MLPClassifier(hidden_layer_sizes=(50,)), "XGBoost": XGBClassifier(use_label_encoder=False, eval_metric...
breast_cancer_data.target_names.tolist() # split data into train and test from sklearn.model_selection import train_test_split x_train, x_test, y_train, y_test = train_test_split(breast_cancer_data.data, breast_cancer_data.target, test_size=0.2, random_state=0) clf = svm.SVC(gam...
("svc", SVC(kernel='rbf', gamma=gamma)) ]) svc = RBFKernelSVC(gamma=1.0) svc.fit(X, y) """ Out[4]: Pipeline(memory=None, steps=[('std_scaler', StandardScaler(copy=True, with_mean=True, with_std=True)), ('svc', SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,...
python中SVC代码如下: 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) ...
函数原型:sklearn.svm.svc(C=1.0, kernel='rbf', degree=3, gamma='auto', coef0=0.0, shrinking=True, probability=False, tol=1e-3, cache_size=200, class_weight=None, verbose=False, max_iter=-1, decision_function_shape='ovr', random_state=None) ...
pca = RandomizedPCA(n_components=150, whiten=True, random_state=42)svc = SVC(kernel='rbf', class_weight='balanced')model = make_pipeline(pca, svc) 为了测试分类器的训练效果,将数据集分解成训练集和测试集进行交叉检验: from sklearn.cross_validation import train_test_splitXtrain, Xtest, ytrai...
('svm_clf', SVC()), ('dt_clf', DecisionTreeClassifier()) ], voting='hard') voting_clf.fit(X_train, y_train) """ Out[13]: VotingClassifier(estimators=[('log_clf', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True, ...
SVC().get_params() 输出的就是SVM算法可以调节的参数以及系统默认的参数值。每个参数的具体含义会在以后的文章中介绍。 {'C': 1.0, 'cache_size': 200, 'class_weight': None, 'coef0': 0.0, 'decision_function_shape': 'ovr', 'degree': 3, 'gamma': 'auto', 'kernel': 'rbf', 'max_iter...
-b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0) -wi weight : set the parameter C of class i to weight*C, for C-SVC (default 1) -v n: n-fold cross validation mode ...