Compact classification ensemble
cens = compact(ens)
a compact version of
cens = compact(
ens. You can predict classifications
cens exactly as you can using
cens does not contain training data,
you cannot perform some actions, such as cross validation.
A classification ensemble created with
A compact classification ensemble.
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.
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 = 392654 352622
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.