compact

Compact classification ensemble

Syntax

cens = compact(ens)


Description

cens = compact(ens) creates a compact version of ens. You can predict classifications using cens exactly as you can using ens. However, since cens does not contain training data, you cannot perform some actions, such as cross validation.

Input Arguments

ens	A classification ensemble created with fitcensemble.

Output Arguments

cens	A compact classification ensemble. cens has class CompactClassificationEnsemble.

Examples

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.

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

      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.

Extended Capabilities

GPU Arrays

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).