你会喜欢这个答案的……

查看您的代码并注意您正在调用评分函数，并且每次您传入完全相同的值时，即它们都在吐出 lin_svc.score()。尝试在四个相应的 fit 调用下方交织四个评分调用，您应该会看到结果中所需的变化。

# we create an instance of SVM and fit out data. We do not scale our
# data since we want to plot the support vectors
#rbf is for gaussian
C = 1.0  # SVM regularization parameter

svc = svm.SVC(kernel='linear', C=C).fit(X_train, y_train)
print svc.score(X_test, y_test)

rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X_train, y_train)
print rbf_svc.score(X_test, y_test)

poly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X_train, y_train)
print poly_svc.score(X_test, y_test)

lin_svc = svm.LinearSVC(C=C).fit(X_train, y_train)
print lin_svc.score(X_test, y_test)

同样，您也在下面做同样的事情。

希望这可以帮助！