ML之RF：基于Matlab利用RF算法實現根據乳腺腫瘤特征向量高精度(better)預測腫瘤的是惡性還是良性

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%RF：RF實現根據乳腺腫瘤特征向量高精度(better)預測腫瘤的是惡性還是良性load?data.mata =?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)?'%']);figureindex =?find(T_sim ~= T_test); plot(votes(index,1),votes(index,2),'r*') hold?onindex =?find(T_sim == T_test); plot(votes(index,1),votes(index,2),'bo') hold?onlegend('紅色*是錯誤分類樣本','藍色空心圓是正確分類樣本')plot(0:500,500:-1:0,'r-.') hold?onplot(0:500,0:500,'r-.') hold?online([100 400 400 100 100],[100 100 400 400 100])xlabel('輸出為類別1的決策樹棵數') ylabel('輸出為類別2的決策樹棵數') title('隨機森林分類器性能分析—Jason niu')???Accuracy =?zeros(1,20); for?i?= 50:50:1000iaccuracy =?zeros(1,100);for?k = 1:100model = 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);endAccuracy(i/50) =?mean(accuracy); endfigure plot(50:50:1000,Accuracy) xlabel('隨機森林中決策樹棵數') ylabel('分類正確率') title('隨機森林中決策樹棵數對性能的影響—Jason niu')

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RF：RF實現根據乳腺腫瘤特征向量高精度(better)預測腫瘤的是惡性還是良性

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