MATLAB決策樹(shù)算法(共3頁(yè))

1、MATLAB決策樹(shù)算法% I. 清空環(huán)境變量clear allclcwarning off% II. 導(dǎo)入數(shù)據(jù) 第一列是序號(hào) 第二列是良性還是惡性（乳腺癌） 后面是特征屬性30個(gè)load data.mat% 1. 隨機(jī)產(chǎn)生訓(xùn)練集/測(cè)試集a = randperm(569);Train = data(a(1:500),:); %產(chǎn)生500個(gè)訓(xùn)練集Test = data(a(501:end),:); %剩下的是測(cè)試集 69個(gè)% 2. 訓(xùn)練數(shù)據(jù)P_train = Train(:,3:end);T_train = Train(:,2);% 3. 測(cè)試數(shù)據(jù)P_test = Test(:,3:end);T_

2、test = Test(:,2);% III. 創(chuàng)建決策樹(shù)分類(lèi)器ctree = ClassificationTree.fit(P_train,T_train);% 1. 查看決策樹(shù)視圖view(ctree);view(ctree,mode,graph);% IV. 仿真測(cè)試T_sim = predict(ctree,P_test);% V. 結(jié)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

3、= 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(病例總數(shù)： num2str(569). 良性： num2str(total_B). 惡性： num2str(tot

4、al_M);disp(訓(xùn)練集病例總數(shù)： num2str(500). 良性： num2str(count_B). 惡性： num2str(count_M);disp(測(cè)試集病例總數(shù)： num2str(69). 良性： num2str(number_B). 惡性： num2str(number_M);disp(良性乳腺腫瘤確診： num2str(number_B_sim). 誤診： num2str(number_B - number_B_sim). 確診率p1= num2str(number_B_sim/number_B*100) %);disp(惡性乳腺腫瘤確診： num2str(number_

5、M_sim). 誤診： num2str(number_M - number_M_sim). 確診率p2= num2str(number_M_sim/number_M*100) %); % VI. 葉子節(jié)點(diǎn)含有的最小樣本數(shù)對(duì)決策樹(shù)性能的影響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);endplot(leafs,e

6、rr);xlabel(葉子節(jié)點(diǎn)含有的最小樣本數(shù));ylabel(交叉驗(yàn)證誤差);title(葉子節(jié)點(diǎn)含有的最小樣本數(shù)對(duì)決策樹(shù)性能的影響)% VII. 設(shè)置minleaf為13，產(chǎn)生優(yōu)化決策樹(shù)OptimalTree = ClassificationTree.fit(P_train,T_train,minleaf,13);view(OptimalTree,mode,graph)% 1. 計(jì)算優(yōu)化后決策樹(shù)的重采樣誤差和交叉驗(yàn)證誤差resubOpt = resubLoss(OptimalTree)lossOpt = kfoldLoss(crossval(OptimalTree)% 2. 計(jì)算優(yōu)化前決策樹(shù)的重采樣誤差和交叉驗(yàn)證誤差resubDefault = resubLoss(ctree)lossDefault = kfoldLoss(crossval(ctree)% VIII. 剪枝,bestlevel = cvLoss(ctree,subtrees,all,treesize,min)cptree = prune(ctree,Level,bestlevel);view(cptree,