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1、精选优质文档-倾情为你奉上MATLAB决策树算法% I. 清空环境变量clear allclcwarning off% II. 导入数据 第一列是序号 第二列是良性还是恶性（乳腺癌） 后面是特征属性30个load data.mat% 1. 随机产生训练集/测试集a = randperm(569);Train = data(a(1:500),:); %产生500个训练集Test = data(a(501:end),:); %剩下的是测试集 69个% 2. 训练数据P_train = Train(:,3:end);T_train = Train(:,2);% 3. 测试数据P_test = Tes
2、t(:,3:end);T_test = Test(:,2);% III. 创建决策树分类器ctree = ClassificationTree.fit(P_train,T_train);% 1. 查看决策树视图view(ctree);view(ctree,mode,graph);% IV. 仿真测试T_sim = predict(ctree,P_test);% V. 结果分析count_B = length(find(T_train = 1);count_M = length(find(T_train = 2);rate_B = count_B / 500;rate_M = count_M /
3、 500;total_B = length(find(data(:,2) = 1);total_M = length(find(data(:,2) = 2);number_B = length(find(T_test = 1);number_M = length(find(T_test = 2);number_B_sim = length(find(T_sim = 1 & T_test = 1);number_M_sim = length(find(T_sim = 2 & T_test = 2);disp(病例总数： num2str(569). 良性： num2str(total_B). 恶性
4、： num2str(total_M);disp(训练集病例总数： num2str(500). 良性： num2str(count_B). 恶性： num2str(count_M);disp(测试集病例总数： num2str(69). 良性： num2str(number_B). 恶性： num2str(number_M);disp(良性乳腺肿瘤确诊： num2str(number_B_sim). 误诊： num2str(number_B - number_B_sim). 确诊率p1= num2str(number_B_sim/number_B*100) %);disp(恶性乳腺肿瘤确诊： nu
5、m2str(number_M_sim). 误诊： num2str(number_M - number_M_sim). 确诊率p2= num2str(number_M_sim/number_M*100) %); % VI. 叶子节点含有的最小样本数对决策树性能的影响leafs = logspace(1,2,10);N = numel(leafs);err = zeros(N,1);for n = 1:N t = ClassificationTree.fit(P_train,T_train,crossval,on,minleaf,leafs(n); err(n) = kfoldLoss(t);en
6、dplot(leafs,err);xlabel(叶子节点含有的最小样本数);ylabel(交叉验证误差);title(叶子节点含有的最小样本数对决策树性能的影响)% VII. 设置minleaf为13，产生优化决策树OptimalTree = ClassificationTree.fit(P_train,T_train,minleaf,13);view(OptimalTree,mode,graph)% 1. 计算优化后决策树的重采样误差和交叉验证误差resubOpt = resubLoss(OptimalTree)lossOpt = kfoldLoss(crossval(OptimalTree
7、)% 2. 计算优化前决策树的重采样误差和交叉验证误差resubDefault = resubLoss(ctree)lossDefault = kfoldLoss(crossval(ctree)% VIII. 剪枝,bestlevel = cvLoss(ctree,subtrees,all,treesize,min)cptree = prune(ctree,Level,bestlevel);view(cptree,mode,graph)% 1. 计算剪枝后决策树的重采样误差和交叉验证误差resubPrune = resubLoss(cptree)lossPrune = kfoldLoss(crossval(cptree)专心-专注-专业