如何在scikit学习中绘制多元回归的最佳拟合平面？

import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import PolynomialFeatures

X = np.array([[1, 2], [2, 3], [3, 4], [4, 5], [5, 6]])
y = np.array([3, 4, 5, 6, 7])

poly = PolynomialFeatures(degree=2)  # 设置多项式的阶数
X_poly = poly.fit_transform(X)  # 转换特征为多项式特征

regressor = LinearRegression()
regressor.fit(X_poly, y)

x1_grid, x2_grid = np.meshgrid(np.arange(1, 6, 0.1), np.arange(2, 7, 0.1))
X_grid = np.column_stack((x1_grid.ravel(), x2_grid.ravel()))
X_poly_grid = poly.transform(X_grid)
y_pred_grid = regressor.predict(X_poly_grid)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(X[:, 0], X[:, 1], y, color='red', label='Actual')
ax.plot_surface(x1_grid, x2_grid, y_pred_grid.reshape(x1_grid.shape), alpha=0.5, cmap='viridis', label='Best Fit')
ax.set_xlabel('X1')
ax.set_ylabel('X2')
ax.set_zlabel('y')
ax.legend()
plt.show()