Reduce size of classification ensemble model
You can predict classifications using the
cens in the same way as when you use
ens. However, because
does not contain training data, you cannot perform certain tasks, such as
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 464631 423531
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).
Introduced in R2011a