将adaboost应用于逻辑斯蒂回归 python实现

Adaboost可以用于逻辑斯蒂回归的分类问题中，具体实现步骤如下：

1.导入需要的库和数据集

import numpy as np
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import train_test_split

# 创建一个二分类的数据集
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, random_state=42)
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

2. 初始化Adaboost分类器和逻辑斯蒂回归分类器

# 初始化Adaboost分类器
ada = AdaBoostClassifier(n_estimators=10, random_state=42)
# 初始化逻辑斯蒂回归分类器
logistic = LogisticRegression(random_state=42)

3. 将逻辑斯蒂回归分类器添加到Adaboost分类器中

ada.fit(X_train, y_train)
ada.estimators_.append(logistic)
ada.n_estimators_ += 1

4. 训练Adaboost分类器并进行预测

# 训练Adaboost分类器
ada.fit(X_train, y_train)
# 对测试集进行预测
y_pred = ada.predict(X_test)

完整代码如下：

import numpy as np
from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import AdaBoostClassifier
from sklearn.model_selection import train_test_split

# 创建一个二分类的数据集
X, y = make_classification(n_samples=1000, n_features=10, n_informative=5, n_redundant=0, random_state=42)
# 将数据集分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# 初始化Adaboost分类器和逻辑斯蒂回归分类器
ada = AdaBoostClassifier(n_estimators=10, random_state=42)
logistic = LogisticRegression(random_state=42)

# 将逻辑斯蒂回归分类器添加到Adaboost分类器中
ada.fit(X_train, y_train)
ada.estimators_.append(logistic)
ada.n_estimators_ += 1

# 训练Adaboost分类器并进行预测
ada.fit(X_train, y_train)
y_pred = ada.predict(X_test)

# 输出模型的准确率
accuracy = np.mean(y_pred == y_test)
print("Accuracy:", accuracy)