如何使用Python中的决策树进行异常检测

from sklearn.tree import DecisionTreeClassifier
import numpy as np

# 构建决策树模型
X = np.array([[1, 0], [0, 1], [0, 0], [1, 1], [0, 0], [1, 1], [1, 0]])
y = np.array([1, 1, 0, 1, 0, 1, 1])
dt = DecisionTreeClassifier()
dt.fit(X, y)

# 预测数据是否为异常数据
sample1 = np.array([[0, 0]])  # 异常数据
sample2 = np.array([[1, 1]])  # 正常数据
pred1 = dt.predict(sample1)
pred2 = dt.predict(sample2)
print("sample1:", sample1, "is anomaly:", pred1[0])
print("sample2:", sample2, "is anomaly:", pred2[0])

sample1: [[0 0]] is anomaly: 0
sample2: [[1 1]] is anomaly: 1