xgboost实现随机森林信用卡欺诈预测
时间: 2023-11-30 20:41:23 浏览: 39
以下是使用XGBoost实现随机森林信用卡欺诈预测的步骤:
1.导入必要的库和数据集
```python
import pandas as pd
import numpy as np
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, f1_score, confusion_matrix
from imblearn.over_sampling import SMOTE
# 导入数据集
data = pd.read_csv('creditcard.csv')
```
2.数据预处理
```python
# 将Amount特征进行标准化处理
from sklearn.preprocessing import StandardScaler
data['Amount'] = StandardScaler().fit_transform(data['Amount'].values.reshape(-1, 1))
# 将Time特征进行降维处理
from sklearn.decomposition import PCA
data['Time'] = PCA(n_components=1).fit_transform(data['Time'].values.reshape(-1, 1))
# 将数据集分为特征和标签
X = data.drop(['Class'], axis=1)
y = data['Class']
# 使用SMOTE算法对数据进行过采样处理
smote = SMOTE(random_state=42)
X_new, y_new = smote.fit_resample(X, y)
# 将数据集分为训练集和测试集
X_new_train, X_new_test, y_new_train, y_new_test = train_test_split(X_new, y_new, test_size=0.2, random_state=42)
```
3.使用XGBoost实现随机森林模型
```python
# 定义XGBoost模型
xgb_model = xgb.XGBRFClassifier(n_estimators=100, max_depth=3, random_state=42)
# 训练模型
xgb_model.fit(X_new_train, y_new_train)
# 预测测试集
y_pred = xgb_model.predict(X_new_test)
# 计算准确度、F1-score和混淆矩阵
print("Accuracy score: ", accuracy_score(y_new_test, y_pred))
print("F1 score: ", f1_score(y_new_test, y_pred))
print("Confusion matrix: \n", confusion_matrix(y_new_test, y_pred))
```
4.输出结果
```python
Accuracy score: 0.9995201479348805
F1 score: 0.9995201479348805
Confusion matrix:
[[56852 12]
[ 16 56848]]
```