Compare the size of a classification ensemble for the Fisher iris data to the compact version of the ensemble.

Load the Fisher iris data set.

load fisheriris

Train an ensemble of 100 boosted classification trees using AdaBoostM2.

t = templateTree('MaxNumSplits',1); % Weak learner template tree object
ens = fitcensemble(meas,species,'Method','AdaBoostM2','Learners',t);

Create a compact version of ens and compare ensemble sizes.

cens = compact(ens);
b = whos('ens');  % b.bytes = size of ens
c = whos('cens'); % c.bytes = size of cens
[b.bytes c.bytes] % Shows cens uses less memory

ans = 1×2

      446971      406462

The compact version of the ensemble uses less memory than the full ensemble. Note that the ensemble sizes can vary slightly, depending on your operating system.