ML之RF：基于Matlab利用RF算法实现根据乳腺肿瘤特征向量高精度(better)预测肿瘤的是恶性还是良性

目录

输出结果

实现代码

输出结果

更新……

实现代码

%RF：RF实现根据乳腺肿瘤特征向量高精度(better)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);

model = classRF_train(P_train,T_train);

[T_sim,votes] = classRF_predict(P_test,model);

count_B = length(find(T_train == 1));

count_M = length(find(T_train == 2));

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) '%']);

figure

index = find(T_sim ~= T_test);

plot(votes(index,1),votes(index,2),'r*')

hold on

index = find(T_sim == T_test);

plot(votes(index,1),votes(index,2),'bo')

hold on

legend('红色*是错误分类样本','蓝色空心圆是正确分类样本')

plot(0:500,500:-1:0,'r-.')

hold on

plot(0:500,0:500,'r-.')

hold on

line([100 400 400 100 100],[100 100 400 400 100])

xlabel('输出为类别1的决策树棵数')

ylabel('输出为类别2的决策树棵数')

title('随机森林分类器性能分析—Jason niu')

Accuracy = zeros(1,20);

for i = 50:50:1000

i

accuracy = zeros(1,100);

for k = 1:100

model = classRF_train(P_train,T_train,i);

T_sim = classRF_predict(P_test,model);

accuracy(k) = length(find(T_sim == T_test)) / length(T_test);

end

Accuracy(i/50) = mean(accuracy);

end

figure

plot(50:50:1000,Accuracy)

xlabel('随机森林中决策树棵数')

ylabel('分类正确率')

title('随机森林中决策树棵数对性能的影响—Jason niu')

相关文章

RF：RF实现根据乳腺肿瘤特征向量高精度(better)预测肿瘤的是恶性还是良性