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Classification margins by resubstitution


margin = resubMargin(ens)
margin = resubMargin(ens,Name,Value)


margin = resubMargin(ens) returns the classification margin obtained by ens on its training data.

margin = resubMargin(ens,Name,Value) calculates margins with additional options specified by one or more Name,Value pair arguments.

Input Arguments


A classification ensemble created with fitcensemble.

Name-Value Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.


Indices of weak learners in the ensemble ranging from 1 to NumTrained. resubMargin uses only these learners for calculating margin.

Default: 1:NumTrained

Output Arguments


A numeric column-vector of length size(ens.X,1) containing the classification margins.


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Find the resubstitution margins for an ensemble that classifies the Fisher iris data.

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);

Find the resubstitution margins.

margin = resubMargin(ens);
[min(margin) mean(margin) max(margin)]
ans = 1×3

   -0.5674    3.2486    4.6245

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Extended Capabilities