糖尿病数据集回归分析，实现5个折叠。PYTHON代码

糖尿病数据集回归分析通常用于预测患者的血糖水平、血压等指标，基于机器学习模型如线性回归、决策树或者神经网络。在Python中，我们可以使用Scikit-learn库来实现这个任务，并通过交叉验证（例如K折交叉验证）提高模型的泛化能力。以下是一个简单的例子，展示如何使用K-Fold对Pandas DataFrame加载的糖尿病数据进行五折交叉验证的线性回归：

import pandas as pd
from sklearn import datasets
from sklearn.model_selection import train_test_split, KFold
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error

# 加载糖尿病数据集
diabetes = datasets.load_diabetes()
X = diabetes.data
y = diabetes.target

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

# 创建一个KFold实例
kfold = KFold(n_splits=5, shuffle=True, random_state=42)

# 定义一个函数，用于执行回归并计算MSE
def run_regression(model, X, y, kfold=kfold):
    mse_scores = []
    for train_index, val_index in kfold.split(X):
        X_train_, y_train_ = X[train_index], y[train_index]
        X_val, y_val = X[val_index], y[val_index]

        model.fit(X_train_, y_train_)
        y_pred = model.predict(X_val)
        mse = mean_squared_error(y_val, y_pred)
        mse_scores.append(mse)
    return mse_scores

# 实例化线性回归模型
lr = LinearRegression()

# 执行5折交叉验证
mse_per_fold = run_regression(lr, X_train, y_train)

# 计算平均MSE
average_mse = sum(mse_per_fold) / len(mse_per_fold)
print(f"Average Mean Squared Error across all folds: {average_mse}")

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