MATLAB决策树算法

MATLAB决策树算法%%I.清空环境变量clearallclcwarningoff%%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loaddata.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);forn=1:Nt=ClassificationTree.fit(P_train,T_train,'crossval','on','minleaf',leafs(n));err(n)=kfoldLoss(t);endplot(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))