Classification edge by resubstitution


edge = resubEdge(ens)
edge = resubEdge(ens,Name,Value)


edge = resubEdge(ens) returns the classification edge obtained by ens on its training data.

edge = resubEdge(ens,Name,Value) calculates edge with additional options specified by one or more Name,Value pair arguments. You can specify several name-value pair arguments in any order as Name1,Value1,…,NameN,ValueN.

Input Arguments


A classification ensemble created with fitcensemble.

Name-Value Pair 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. resubEdge uses only these learners for calculating edge.

Default: 1:NumTrained


Character vector or string scalar representing the meaning of the output edge:

  • 'ensemble'edge is a scalar value, the loss for the entire ensemble.

  • 'individual'edge is a vector with one element per trained learner.

  • 'cumulative'edge is a vector in which element J is obtained by using learners 1:J from the input list of learners.

Default: 'ensemble'

Output Arguments


Classification edge obtained by ens by resubstituting the training data into the calculation of edge. Classification edge is classification margin averaged over the entire data. edge can be a scalar or vector, depending on the setting of the mode name-value pair.


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

Load the sample 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 edge.

edge = resubEdge(ens) 
edge = 3.2486

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