给一个模型融合的Python代码

以下是一个模型融合的Python代码示例：

# 导入所需的库和模型
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# 加载数据集
data = pd.read_csv("data.csv")

# 分离特征和目标变量
X = data.drop("target",axis=1)
y = data["target"]

# 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)

# 定义三个不同的分类器
clf1 = LogisticRegression()
clf2 = DecisionTreeClassifier()
clf3 = RandomForestClassifier()

# 训练三个分类器
clf1.fit(X_train, y_train)
clf2.fit(X_train, y_train)
clf3.fit(X_train, y_train)

# 预测测试集结果并计算准确率
pred1 = clf1.predict(X_test)
pred2 = clf2.predict(X_test)
pred3 = clf3.predict(X_test)

acc1 = accuracy_score(y_test, pred1)
acc2 = accuracy_score(y_test, pred2)
acc3 = accuracy_score(y_test, pred3)

print("Logistic Regression accuracy:", acc1)
print("Decision Tree accuracy:", acc2)
print("Random Forest accuracy:", acc3)

# 定义一个投票分类器，将三个分类器融合起来
from sklearn.ensemble import VotingClassifier

estimators = [('lr', clf1), ('dt', clf2), ('rf', clf3)]
ensemble = VotingClassifier(estimators)

# 训练投票分类器
ensemble.fit(X_train, y_train)

# 预测测试集结果并计算准确率
pred_ensemble = ensemble.predict(X_test)
acc_ensemble = accuracy_score(y_test, pred_ensemble)

print("Ensemble accuracy:", acc_ensemble)

在这个示例中，我们加载了一个二分类数据集，然后使用三个不同的分类器（逻辑回归、决策树和随机森林）对数据进行训练和预测。然后，我们定义了一个投票分类器，将三个分类器融合起来，并使用训练集对其进行训练。最后，我们使用测试集评估了每个分类器的准确率和投票分类器的准确率。