在numpy中使用集成方案的问题

import numpy as np
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# 生成示例数据集
X, y = make_classification(n_samples=1000, n_features=20, random_state=42)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 创建基础模型
base_model = DecisionTreeClassifier(random_state=42)

# 创建Bagging集成模型
bagging_model = BaggingClassifier(base_estimator=base_model, n_estimators=10, random_state=42)

# 训练模型
bagging_model.fit(X_train, y_train)

# 预测
y_pred = bagging_model.predict(X_test)

# 评估模型
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy}")