%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:N
    t = 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))

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