removeLearners

Remove members of compact classification ensemble

Syntax

cens1 = removeLearners(cens,idx)


Description

cens1 = removeLearners(cens,idx) creates a compact classification ensemble identical to cens only without the ensemble members in the idx vector.

Input Arguments

cens	Compact classification ensemble, constructed with compact.
idx	Vector of positive integers with entries from 1 to cens.NumTrained, where cens.NumTrained is the number of members in cens. cens1 contains all members of cens except those with indices in idx. Typically, you set idx = j:cens.NumTrained for some positive integer j.

Output Arguments

cens1

Compact classification ensemble, identical to cens except cens1 does not contain those members of cens with indices in idx.

Examples

Remove Learners from an Ensemble

Create a compact classification ensemble. Compact it further by removing members of the ensemble.

Load the ionosphere data set.

load ionosphere

Train a classification ensemble for the ionosphere data using AdaBoostM1. Specify tree stumps as the weak learners.

t = templateTree('MaxNumSplits',1);
ens = fitcensemble(X,Y,'Method','AdaBoostM1','Learners',t);

Create a compact classification ensemble cens from ens.

cens = compact(ens);

Remove the last 50 members of the ensemble.

idx = cens.NumTrained-49:cens.NumTrained;
cens1 = removeLearners(cens,idx);

Tips

• Typically, set cens1 equal to cens to retain just one ensemble.

• Removing learners reduces the memory used by the ensemble and speeds up its predictions.

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).