xgboost实现随机森林的信用卡欺诈预测
时间: 2023-11-26 13:47:25 浏览: 40
以下是使用XGBoost实现随机森林的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.combine import SMOTEENN
from imblearn.under_sampling import RandomUnderSampler
# 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和Amount特征合并为一个特征,并删除原有的Time和Amount特征
data['Time_Amount'] = data['Time'] + data['Amount']
data.drop(['Time', 'Amount'], axis=1, inplace=True)
# 将Class列移动到最后一列
cols = list(data.columns)
cols.remove('Class')
cols.append('Class')
data = data[cols]
# 将数据集分为特征和标签
X = data.iloc[:, :-1]
y = data.iloc[:, -1]
# 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)
```
3.使用SMOTEENN算法进行过采样和欠采样
```python
# 使用SMOTEENN算法进行过采样和欠采样
smote_enn = SMOTEENN(random_state=42)
X_new_train, y_new_train = smote_enn.fit_resample(X_train, y_train)
# 使用RandomUnderSampler算法进行欠采样
rus = RandomUnderSampler(random_state=42)
X_new_2_train, y_new_2_train = rus.fit_resample(X_train, y_train)
```
4.使用XGBoost算法进行模型训练和预测
```python
# 定义XGBoost模型
xgb_model = xgb.XGBRFClassifier(n_estimators=100, max_depth=3, random_state=42)
# 使用SMOTEENN算法进行过采样和欠采样后的数据进行模型训练和预测
xgb_model.fit(X_new_train, y_new_train)
y_pred = xgb_model.predict(X_test)
# 输出模型评估指标
print('Accuracy:', accuracy_score(y_test, y_pred))
print('F1-score:', f1_score(y_test, y_pred))
print('Confusion matrix:', confusion_matrix(y_test, y_pred))
# 使用RandomUnderSampler算法进行欠采样后的数据进行模型训练和预测
xgb_model.fit(X_new_2_train, y_new_2_train)
y_pred_2 = xgb_model.predict(X_test)
# 输出模型评估指标
print('Accuracy with RandomUnderSampler:', accuracy_score(y_test, y_pred_2))
print('F1-score with RandomUnderSampler:', f1_score(y_test, y_pred_2))
print('Confusion matrix with RandomUnderSampler:', confusion_matrix(y_test, y_pred_2))
```