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

Posted 2020-10-24 一个处女座的IT

%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))