请给出上面这个问题的具体代码
时间: 2024-02-13 07:03:17 浏览: 18
好的,下面是一个简单的实现代码,仅供参考:
1. 数据预处理:
```
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.model_selection import train_test_split
# 读取信用卡交易数据
data = pd.read_csv('creditcard.csv')
# 将数据分为训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(data.drop('Class', axis=1), data['Class'], test_size=0.2, random_state=42)
# 对数据进行归一化处理
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)
```
2. 线性模型:
```
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, precision_score, recall_score, f1_score
# 训练逻辑回归模型
lr_model = LogisticRegression()
lr_model.fit(X_train, y_train)
# 对测试集进行预测
y_pred_lr = lr_model.predict(X_test)
# 评价指标
print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred_lr))
print('Accuracy:', accuracy_score(y_test, y_pred_lr))
print('Precision:', precision_score(y_test, y_pred_lr))
print('Recall:', recall_score(y_test, y_pred_lr))
print('F1 Score:', f1_score(y_test, y_pred_lr))
```
3. 非线性模型:
```
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
# 训练支持向量机模型
svm_model = SVC()
svm_model.fit(X_train, y_train)
# 对测试集进行预测
y_pred_svm = svm_model.predict(X_test)
# 评价指标
print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred_svm))
print('Accuracy:', accuracy_score(y_test, y_pred_svm))
print('Precision:', precision_score(y_test, y_pred_svm))
print('Recall:', recall_score(y_test, y_pred_svm))
print('F1 Score:', f1_score(y_test, y_pred_svm))
# 训练决策树模型
dt_model = DecisionTreeClassifier()
dt_model.fit(X_train, y_train)
# 对测试集进行预测
y_pred_dt = dt_model.predict(X_test)
# 评价指标
print('Confusion Matrix:\n', confusion_matrix(y_test, y_pred_dt))
print('Accuracy:', accuracy_score(y_test, y_pred_dt))
print('Precision:', precision_score(y_test, y_pred_dt))
print('Recall:', recall_score(y_test, y_pred_dt))
print('F1 Score:', f1_score(y_test, y_pred_dt))
```
4. 模型比较:
我们可以比较两个模型的 F1 值来选择更优的模型。
```
print('F1 Score of Logistic Regression:', f1_score(y_test, y_pred_lr))
print('F1 Score of SVM:', f1_score(y_test, y_pred_svm))
print('F1 Score of Decision Tree:', f1_score(y_test, y_pred_dt))
```
需要注意的是,具体模型的选择和参数调整需要根据具体数据情况和实验结果来进行调整。