如何在MATLAB中进行分层10折交叉验证分类？

% 1. 数据准备
load('data.mat'); % 加载数据集，假设数据集已经准备好
X = data(:, 1:end-1); % 特征矩阵
y = data(:, end); % 目标向量

% 2. 分层抽样
cv = cvpartition(y, 'KFold', 10); % 创建分层抽样对象

% 4. 循环训练和测试
accuracy = zeros(cv.NumTestSets, 1); % 存储每个折的准确率
precision = zeros(cv.NumTestSets, 1); % 存储每个折的精确率
recall = zeros(cv.NumTestSets, 1); % 存储每个折的召回率

for i = 1:cv.NumTestSets
    % 5. 训练模型
    trainIdx = cv.training(i);
    testIdx = cv.test(i);
    X_train = X(trainIdx, :);
    y_train = y(trainIdx);
    model = fitcsvm(X_train, y_train); % 使用支持向量机训练模型（示例）

    % 6. 测试模型
    X_test = X(testIdx, :);
    y_test = y(testIdx);
    y_pred = predict(model, X_test); % 预测测试数据

    % 计算评估指标
    accuracy(i) = sum(y_pred == y_test) / numel(y_test);
    TP = sum(y_pred == 1 & y_test == 1);
    FP = sum(y_pred == 1 & y_test == 0);
    FN = sum(y_pred == 0 & y_test == 1);
    precision(i) = TP / (TP + FP);
    recall(i) = TP / (TP + FN);
end

% 7. 汇总结果
mean_accuracy = mean(accuracy);
mean_precision = mean(precision);
mean_recall = mean(recall);

disp(['平均准确率：', num2str(mean_accuracy)]);
disp(['平均精确率：', num2str(mean_precision)]);
disp(['平均召回率：', num2str(mean_recall)]);