www.gusucode.com > stats 源码程序 matlab案例代码 > stats/PredictOutofBagMediansUsingQuantileRegressionExample.m
%% Predict Out-of-Bag Medians Using Quantile Regression %% % Load the |carsmall| data set. Consider a model that predicts the fuel % economy of a car given its engine displacement. load carsmall %% % Train an ensemble of bagged regression trees using the entire data set. % Specify 100 weak learners and save out-of-bag indices. rng(1); % For reproducibility Mdl = TreeBagger(100,Displacement,MPG,'Method','regression',... 'OOBPrediction','on'); %% % |Mdl| is a |TreeBagger| ensemble. %% % Perform quantile regression to predict the out-of-bag median MPG for all % training observations. oobMedianMPG = oobQuantilePredict(Mdl); %% % |oobMedianMPG| is an |n|-by-1 numeric vector of medians corresponding to the % conditional distribution of the response given the sorted observations in % |Mdl.X|. |n| is the number of observations, |size(Mdl.X,1)|. %% % Sort the observations in ascending order. Plot the observations and % the estimated medians on the same figure. Compare the out-of-bag median % and mean responses. [sX,idx] = sort(Mdl.X); oobMeanMPG = oobPredict(Mdl); figure; plot(Displacement,MPG,'k.'); hold on plot(sX,oobMedianMPG(idx)); plot(sX,oobMeanMPG(idx),'r--'); ylabel('Fuel economy'); xlabel('Engine displacement'); legend('Data','Out-of-bag median','Out-of-bag mean'); hold off;