MATLAB随机森林回归模型：

调用matlab自带的TreeBagger.m

T=textread('E:\datasets-orreview\discretized-regression\10bins\abalone10\matlab\test_abalone10.2');X=textread('E:\datasets-orreview\discretized-regression\10bins\abalone10\matlab\train_abalone10.2');%nTree = round(sqrt(size(X,2)-1));nTree = 50;train_data = X(:,1:end-1);train_label = X(:,end); test_data = T(:,1:end-1);Factor = TreeBagger(nTree, train_data, train_label,'Method','regression');[Predict_label,Scores] = predict(Factor, test_data);%Predict_label=cellfun(@str2num,Predict_label(1:end));MZE = mean(round(Predict_label) ~= T(:,end))MAE = mean(abs(round(Predict_label) - T(:,end)))

　

调用外部函数forestTrain.m来自

T=textread('E:\datasets-orreview\ordinal-regression\ERA\matlab\test_ERA.1');X=textread('E:\datasets-orreview\ordinal-regression\ERA\matlab\train_ERA.1');opts= struct;opts.depth= 9;opts.numTrees= 60;opts.numSplits= 5;opts.verbose= true;opts.classifierID= 2; % weak learners to use. Can be an array for mix of weak learners tootrain_data = X(:,1:end-1);train_label = X(:,end); test_data = T(:,1:end-1);tic;m= forestTrain(train_data, train_label, opts);timetrain= toc;tic;yhatTrain = forestTest(m, test_data);timetest= toc;MZE = mean(round(yhatTrain) ~= T(:,end))MAE = mean(abs(round(yhatTrain) - T(:,end)))