MATLAB决策树算法.pdf

MATLAB 决策树算法%I.清空环境变量 clear all clc warning 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=Test(:,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/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：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：num2str(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);end plot(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)%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)