基于Matlab实现的随机森林算法.zip

clear clc warning off % 导入数据 load data.mat a = randperm(569); Train = data(a(1:500),:); Test = data(a(501:end),:); % 分配训练数据 P_train = Train(:,3:end); T_train = Train(:,2); % 测试数据 P_test = Test(:,3:end); T_test = Test(:,2); % 创建随机森林分类器 model = classRF_train(P_train,T_train,500,3); % 仿真测试 [T_sim, votes] = classRF_predict(P_test, model); % 结果分析 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：' 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：' num2str(number_M_sim/number_M*100) '%']); % 绘图 figure index = find(T_sim ~= T_test); plot(votes(index,1),votes(index,2),'r*') % r 表示red，即用红色* hold on index = find(T_sim == T_test); plot(votes(index,1),votes(index,2),'bo') % b 表示blue，即用绿色o hold on legend('错误分类样本', '正确分类样本') plot(0:500,500:-1:0,'y-.') hold on plot(0:500,0:500,'y-.') hold on line([100 400 400 100 100],[100 100 400 400 100]) xlabel('输出为类别1的决策树颗数') ylabel('输出为类别2的决策树颗数') title('决策树颗数和分类准确率的关系') % % % 随机森林中决策树颗数对性能的影响 % Accuracy = zeros(1,20); % 返回一个1*20 的全为零的矩阵 % for i = 50:50:1000 % % % 每种情况，运行10次，取其平均值 % accuracy = zeros(1,100); % for k = 1:10 % % 创建随机森林 % model = classRF_train(P_train,T_train,i); % % 仿真测试 % 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 % % 绘图 % figure % plot(50:50:1000,Accuracy); % xlabel('随机森林中决策树颗数'); % ylabel('分类正确率'); % title('随机森林中决策树颗数对性能的影响'); %