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Class: ClassificationEnsemble

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 fitensemble.

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 single 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 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.



The edge is the weighted mean value of the classification margin. The weights are the class probabilities in ens.Prior.


The classification margin is the difference between the classification score for the true class and maximal classification score for the false classes. Margin is a column vector with the same number of rows as in the matrix ens.X.

Score (ensemble)

For ensembles, a classification score represents the confidence of a classification into a class. The higher the score, the higher the confidence.

Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on ensemble type. For example:

  • AdaBoostM1 scores range from –∞ to ∞.

  • Bag scores range from 0 to 1.


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

load fisheriris
ens = fitensemble(meas,species,'AdaBoostM2',100,'Tree');
edge = resubEdge(ens)

edge =
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