CART决策树Matlab代码

以下是使用MATLAB编写的CART决策树代码：

% 数据预处理
load ionosphere;
length = size(X, 1);
rng(1); % 可复现
indices = crossvalind('Kfold', length, 5); % 用k折分类法将样本随机分为5部分

% 四份用来训练，一份进行测试
i = 1;
test = (indices == i);
train = ~test;
X_train = X(train, :);
Y_train = Y(train, :);
X_test = X(test, :);
Y_test = Y(test, :);

% 构建CART算法分类树
tree = fitctree(X_train, Y_train);
view(tree, 'Mode', 'graph'); % 生成树图

% 求取规则数量
rules_num = (tree.IsBranchNode == 0);
rules_num = sum(rules_num);

% 使用测试样本进行验证
Cart_result = predict(tree, X_test);
Cart_result = cell2mat(Cart_result);
Y_test = cell2mat(Y_test);
Cart_result = (Cart_result == Y_test);

% 统计准确率
Cart_length = size(Cart_result, 1);
Cart_rate = (sum(Cart_result)) / Cart_length;

disp(\['规则数：' num2str(rules_num)\]);
disp(\['测试样本识别准确率：' num2str(Cart_rate)\]);

这段代码首先加载了ionosphere数据集，并将数据集分为训练集和测试集。然后使用CART算法构建了一个分类树，并生成了树图。接着统计了分类树中的规则数量。最后使用测试样本对分类树进行验证，并计算了测试样本的识别准确率。
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