DT:DT实现根据乳腺肿瘤特征向量高精度预测肿瘤的是恶性还是良性—Jason niu
%DT:DT实现根据乳腺肿瘤特征向量高精度预测肿瘤的是恶性还是良性 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) '%']); disp(['乳腺肿瘤整体预测准确率:' 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: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,误差越大性能越差—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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