我试图用四个特性对训练数据进行预测；我的代码：

from sklearn.cross_validation import train_test_split

X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.33, random_state=42)

# Train
clf = DecisionTreeClassifier()
clf.fit(X_train, y_train)

# Plot the decision boundary
plt.subplot(2, 3, pairidx + 1)

x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, plot_step),
                     np.arange(y_min, y_max, plot_step))

Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
Z = Z.reshape(xx.shape)
cs = plt.contourf(xx, yy, Z, cmap=plt.cm.Paired)

plt.xlabel(iris.feature_names[pair[0]])
plt.ylabel(iris.feature_names[pair[1]])
plt.axis("tight")

# Plot the training points
for i, color in zip(range(n_classes), plot_colors):
    idx = np.where(y == i)
    plt.scatter(X[idx, 0], X[idx, 1], c=color, label=iris.target_names[i],
                cmap=plt.cm.Paired)

plt.axis("tight")

plt.suptitle("Decision surface of a decision tree using paired features")
plt.legend()
plt.show()

在我的预测行：Z=clf。predict（np.c_uxx.ravel（），yy.ravel（））我得到以下错误：

模型的特征数必须与输入匹配。型号n_features为4，输入n_features为2

iris数据是一个150x4的数据集。我如何让这项功能适用于4个功能？