Compact classification ensemble
cens = compact(ens)
creates a compact version of
cens = compact(
ens. You can predict
cens exactly as you can using
ens. However, since
cens does not
contain training data, you cannot perform some actions, such as cross
A classification ensemble created with
A compact classification ensemble.
View Size of 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 = 1×2 447388 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.
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).