考虑以下数据集（生成它的代码在帖子的底部）：

运行以下代码：

from sklearn.svm import SVC
model_2 = SVC(kernel='rbf', degree=2, gamma='auto', C=100)
model_2.fit(X_train, y_train)
print('accuracy (train): %5.2f'%(metric(y_train, model_2.predict(X_train))))
print('accuracy (test): %5.2f'%(metric(y_test, model_2.predict(X_test))))
print('Number of support vectors:', sum(model_2.n_support_))

我得到以下输出：

accuracy (train):  0.64
accuracy (test):  0.26
Number of support vectors: 55

我还尝试了不同程度的多项式内核，并得到了或多或少相同的结果。

那么为什么它做得这么差。我刚刚了解了 SVM，我认为二阶多项式核可以将这些点投影到抛物面上，结果将是线性可分的。我在哪里错了？

参考：这篇文章中的代码片段的起始代码来自这个课程

生成数据的代码：

np.random.seed(0)
data, labels = sklearn.datasets.make_circles()
idx = np.arange(len(labels))
np.random.shuffle(idx)
# train on a random 2/3 and test on the remaining 1/3
idx_train = idx[:2*len(idx)//3]
idx_test = idx[2*len(idx)//3:]
X_train = data[idx_train]
X_test = data[idx_test]

y_train = 2 * labels[idx_train] - 1  # binary -> spin
y_test = 2 * labels[idx_test] - 1

scaler = sklearn.preprocessing.StandardScaler()
normalizer = sklearn.preprocessing.Normalizer()

X_train = scaler.fit_transform(X_train)
X_train = normalizer.fit_transform(X_train)

X_test = scaler.fit_transform(X_test)
X_test = normalizer.fit_transform(X_test)
plt.figure(figsize=(6, 6))
plt.subplot(111)
plt.scatter(data[labels == 0, 0], data[labels == 0, 1], color='navy')
plt.scatter(data[labels == 1, 0], data[labels == 1, 1], color='c')
```