Python实现信用卡欺诈检测：AdaBoost、随机森林和集成学习模型

下载数据集可以使用以下 Python 代码下载数据集：

!pip install kaggle
from google.colab import files
uploaded = files.upload()
import os
os.environ['KAGGLE_USERNAME'] = 'Your Kaggle Username' # 需替换为自己的Kaggle用户名
os.environ['KAGGLE_KEY'] = 'Your Kaggle API Key' # 需替换为自己的Kaggle API Key
!kaggle datasets download -d pierra/credit-card-dataset-svm-classification
!unzip credit-card-dataset-svm-classification.zip

数据集划分可以使用 sklearn 库中的 train_test_split 函数将数据集划分为训练集和测试集，并指定划分比例。

from sklearn.model_selection import train_test_split
import pandas as pd

data = pd.read_csv('creditcard.csv')
X = data.iloc[:, :-1].values # 特征
y = data.iloc[:, -1].values # 标签

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

AdaBoost算法建模和评估可以使用 sklearn 库中的 AdaBoostClassifier 类建立 AdaBoost 模型，并使用测试集评估模型的性能。

from sklearn.ensemble import AdaBoostClassifier
from sklearn.metrics import accuracy_score

clf = AdaBoostClassifier(n_estimators=100)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)

随机森林算法建模和评估可以使用 sklearn 库中的 RandomForestClassifier 类建立随机森林模型，并使用测试集评估模型的性能。

from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print('Accuracy:', acc)

集成学习模型建立和评估可以使用 sklearn 库中的 VotingClassifier 类建立集成学习模型，并使用测试集评估模型的性能。

from sklearn.tree import DecisionTreeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import LinearSVC, SVC
from sklearn.ensemble import VotingClassifier
from sklearn.metrics import accuracy_score

dt = DecisionTreeClassifier()
knn1 = KNeighborsClassifier(n_neighbors=1)
knn3 = KNeighborsClassifier(n_neighbors=3)
svm_linear = LinearSVC()
svm_rbf = SVC(kernel='rbf')

# 多数投票结合原则
clf = VotingClassifier(estimators=[('dt', dt), ('knn1', knn1), ('knn3', knn3), ('svm_linear', svm_linear), ('svm_rbf', svm_rbf)], voting='hard')
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print('Accuracy (Majority Voting):', acc)

# 相对多数投票结合原则
clf = VotingClassifier(estimators=[('dt', dt), ('knn1', knn1), ('knn3', knn3), ('svm_linear', svm_linear), ('svm_rbf', svm_rbf)], voting='soft')
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
acc = accuracy_score(y_test, y_pred)
print('Accuracy (Weighted Voting):', acc)