compact

Reduce size of classification ensemble model

Syntax

cens = compact(ens)

Description

cens = compact(ens) returns a CompactClassificationEnsemble version of the trained classification ensemble model ens.

You can predict classifications using the CompactClassificationEnsemble model object cens in the same way as when you use ens. However, because cens does not contain training data, you cannot perform certain tasks, such as cross-validation.

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

      437194      395134

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.

Input Arguments

ens — Full classification ensemble model

ClassificationEnsemble model object | ClassificationBaggedEnsemble model object

Full classification ensemble model, specified as a ClassificationEnsemble or ClassificationBaggedEnsemble model object trained with fitcensemble.

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).

Version History

Introduced in R2011a