import sklearn.svm clf = sklearn.svm.SVC() data = [(1,1), (1,0), (0,1), (0,0)] targets = [0,1,1,0] clf.fit(data, targets) SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0, degree=3, gamma=0.0, kernel='rbf', max_iter=-1, probability=False, random_state=None, shrinking=True, tol=0.001, verbose=False) clf.predict((0,0))[0] 0