根据提供的数据集（LR.zip），使用SVM与LR分类模型，选取合理的评价指标，编写程序，对结果进行对比。

首先，你需要下载并解压名为"LR.zip"的数据集，然后加载数据并预处理，包括特征提取、缺失值处理、归一化等步骤。对于SVM（支持向量机）和LR（逻辑回归）这两种分类模型，我们通常使用的评价指标有准确率（Accuracy）、精确率（Precision）、召回率（Recall）和F1分数（F1 Score）。以下是基本步骤：

导入必要的库：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score

加载数据：

data = pd.read_csv('LR.zip', compression='zip')

数据预处理：

X = data.drop('target_column', axis=1)  # 假设'target_column'是目标变量
y = data['target_column']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

构建模型并训练： ```python lr_model = LogisticRegression() svm_model = SVC()

lr_model.fit(X_train, y_train) svm_model.fit(X_train, y_train)


5. 预测及评估：
```python
lr_pred = lr_model.predict(X_test)
svm_pred = svm_model.predict(X_test)

accuracy_lr = accuracy_score(y_test, lr_pred)
precision_lr = precision_score(y_test, lr_pred, average='weighted')
recall_lr = recall_score(y_test, lr_pred, average='weighted')
f1_lr = f1_score(y_test, lr_pred, average='weighted')

accuracy_svm = accuracy_score(y_test, svm_pred)
precision_svm = precision_score(y_test, svm_pred, average='weighted')
recall_svm = recall_score(y_test, svm_pred, average='weighted')
f1_svm = f1_score(y_test, svm_pred, average='weighted')

print("Logistic Regression Metrics:")
print("Accuracy:", accuracy_lr)
print("Precision:", precision_lr)
print("Recall:", recall_lr)
print("F1 Score:", f1_lr)

print("\nSupport Vector Machine Metrics:")
print("Accuracy:", accuracy_svm)
print("Precision:", precision_svm)
print("Recall:", recall_svm)
print("F1 Score:", f1_svm)