给定糖尿病数据集使用k近邻模型、决策树模型、贝叶斯模型以及支持向量机模型构建预测模型,并比较每个模型预测的准确率、精确率、召回率等指标，写出代码

在Python中，我们可以使用`sklearn`库中的KNN、DecisionTree、Bayesian（通过朴素贝叶斯算法实现）、和支持向量机(SVM)模型来进行糖尿病数据集的预测，并评估它们的性能。这里是一个简单的示例，假设你有一个名为`diabetes.csv`的数据集：

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, precision_score, recall_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.svm import SVC

# 加载数据
data = pd.read_csv('diabetes.csv')
X = data.drop('Outcome', axis=1)
y = data['Outcome']

# 数据预处理（标准化）
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.2, random_state=42)

# 定义模型列表
models = {
    'KNN': KNeighborsClassifier(),
    'Decision Tree': DecisionTreeClassifier(),
    'Naive Bayes': GaussianNB(),
    'SVM': SVC(probability=True)  # SVM需要probability=True获取概率估计
}

# 训练和评估每个模型
results = {}
for model_name, model in models.items():
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    
    # 计算指标
    acc = accuracy_score(y_test, y_pred)
    prec = precision_score(y_test, y_pred)
    rec = recall_score(y_test, y_pred)
    
    results[model_name] = {'Accuracy': acc, 'Precision': prec, 'Recall': rec}
    
# 打印结果
for model, metrics in results.items():
    print(f"{model}:")
    for metric, value in metrics.items():
        print(f"   {metric}: {value:.2f}")