www.gusucode.com > stats 源码程序 matlab案例代码 > stats/TrainAnBaggedEnsembleOfClassificationTreesExample.m
%% Train Ensemble of Bagged Classification Trees %% % Load Fisher's iris data set. load fisheriris %% % Train an ensemble of bagged classification trees using the entire data % set. Specify |50| weak learners. Store which observations are out of bag % for each tree. rng(1); % For reproducibility Mdl = TreeBagger(50,meas,species,'OOBPrediction','On',... 'Method','classification') %% % |Mdl| is a |TreeBagger| ensemble. %% % |Mdl.Trees| stores a 50-by-1 cell vector of the trained classification % trees (|CompactClassificationTree| model objects) that compose the % ensemble. %% % Plot a graph of the first trained classification tree. view(Mdl.Trees{1},'Mode','graph') %% % By default, |TreeBagger| grows deep trees. %% % |Mdl.OOBIndices| stores the out-of-bag indices as a matrix of logical % values. %% % Plot the out-of-bag error over the number of grown classification trees. figure; oobErrorBaggedEnsemble = oobError(Mdl); plot(oobErrorBaggedEnsemble) xlabel 'Number of grown trees'; ylabel 'Out-of-bag classification error'; %% % The out-of-bag error decreases with the number of grown trees. %% % To label out-of-bag observations, pass |Mdl| to |oobPredict|.