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- from sklearn.datasets import load_breast_cancer
- cancer = load_breast_cancer()
- (X_cancer, y_cancer) = load_breast_cancer(return_X_y = True)
- from sklearn.model_selection import train_test_split
- from sklearn.svm import SVC
- X_train, X_test, y_train, y_test = train_test_split(X_cancer, y_cancer,
- random_state = 0)
- clf = SVC(C=10).fit(X_train, y_train)
- print('Breast cancer dataset (unnormalized features)')
- print('Accuracy of RBF-kernel SVC on training set: {:.2f}'
- .format(clf.score(X_train, y_train)))
- print('Accuracy of RBF-kernel SVC on test set: {:.2f}'
- .format(clf.score(X_test, y_test)))
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