MAT之DT：DT實現根據乳腺腫瘤特征向量高精度預測腫瘤的是惡性還是良性

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%DT：DT實現根據乳腺腫瘤特征向量高精度預測腫瘤的是惡性還是良性 load?data.mat?a =?randperm(569); Train = data(a(1:500),:); Test = data(a(501:end),:);P_train = Train(:,3:end); T_train = Train(:,2);P_test = Test(:,3:end); T_test = Test(:,2);ctree = ClassificationTree.fit(P_train,T_train);view(ctree);?????????????? view(ctree,'mode','graph');T_sim = predict(ctree,P_test);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(['病例總數：'?num2str(569)...'? 良性：'?num2str(total_B)...'? 惡性：'?num2str(total_M)]); disp(['訓練集病例總數：'?num2str(500)...'? 良性：'?num2str(count_B)...'? 惡性：'?num2str(count_M)]); disp(['測試集病例總數：'?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_M_sim)...'? 誤診：'?num2str(number_M - number_M_sim)...'? 確診率p2='?num2str(number_M_sim/number_M*100)?'%']); disp(['乳腺腫瘤整體預測準確率：'?num2str((number_M_sim/number_M*100+number_B_sim/number_B*100)/2)?'%']);leafs =?logspace(1,2,10);N =?numel(leafs);err =?zeros(N,1); for?n = 1:Nt = ClassificationTree.fit(P_train,T_train,'crossval','on','minleaf',leafs(n));?err(n) = kfoldLoss(t); end plot(leafs,err); xlabel('葉子節點含有的最小樣本數'); ylabel('交叉驗證誤差'); title('葉子節點含有的最小樣本數對決策樹性能的影響,誤差越大性能越差—Jason niu')OptimalTree = ClassificationTree.fit(P_train,T_train,'minleaf',13);? view(OptimalTree,'mode','graph')resubOpt = resubLoss(OptimalTree) lossOpt = kfoldLoss(crossval(OptimalTree))resubDefault = resubLoss(ctree) lossDefault = kfoldLoss(crossval(ctree))[~,~,~,bestlevel] = cvLoss(ctree,'subtrees','all','treesize','min') cptree = prune(ctree,'Level',bestlevel); view(cptree,'mode','graph')resubPrune = resubLoss(cptree) lossPrune = kfoldLoss(crossval(cptree))

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DT：DT實現根據乳腺腫瘤特征向量高精度預測腫瘤的是惡性還是良性

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