利用make_classification数据集从MLPClassifier绘制三维决策边界

from sklearn.datasets import make_classification
from sklearn.neural_network import MLPClassifier
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
import numpy as np

X, y = make_classification(n_samples=100, n_features=3, n_informative=2, n_redundant=1, n_classes=2, random_state=1)

clf = MLPClassifier(hidden_layer_sizes=(100,), max_iter=1000, random_state=1)
clf.fit(X, y)

fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')

# 生成网格点
x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
z_min, z_max = X[:, 2].min() - 1, X[:, 2].max() + 1
xx, yy, zz = np.meshgrid(np.arange(x_min, x_max, 0.1), np.arange(y_min, y_max, 0.1), np.arange(z_min, z_max, 0.1))

# 预测网格点的类别
Z = clf.predict(np.c_[xx.ravel(), yy.ravel(), zz.ravel()])
Z = Z.reshape(xx.shape)

# 绘制决策边界
ax.scatter(X[:, 0], X[:, 1], X[:, 2], c=y, cmap=plt.cm.Set1, edgecolor='k')
ax.contour3D(xx, yy, zz, Z, cmap=plt.cm.Set1, alpha=0.5)

# 设置坐标轴标签
ax.set_xlabel('Feature 1')
ax.set_ylabel('Feature 2')
ax.set_zlabel('Feature 3')

# 显示图形
plt.show()