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Resubstitution classification edge for classification ensemble model



edge = resubEdge(ens) returns the resubstitution Classification Edge (edge) for the trained classification ensemble model ens using the training data stored in ens.X and the corresponding true class labels stored in ens.Y. The classification edge is the Classification Margin averaged over the entire data set. edge can be a scalar or vector, depending on the setting of the Mode name-value argument.

edge = resubEdge(ens,Name=Value) specifies additional options using one or more name-value arguments. For example, you can specify the indices of the weak learners to use for calculating the loss, select the aggregation level for the output, and perform computations in parallel.


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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

Input Arguments

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Classification ensemble model, specified as a ClassificationEnsemble model object trained with fitcensemble.

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: resubEdge(ens,Learners=[1 2 3 5],UseParallel=true) specifies to use the first, second, third, and fifth learners in the ensemble in resubEdge, and to perform computations in parallel.

Indices of weak learners in the ensemble to use in resubEdge, specified as a vector of positive integers in the range [1:ens.NumTrained]. By default, all learners are used.

Example: Learners=[1 2 4]

Data Types: single | double

Aggregation level for the output, specified as "ensemble", "individual", or "cumulative".

"ensemble"The output is a scalar value, the loss for the entire ensemble.
"individual"The output is a vector with one element per trained learner.
"cumulative"The output is a vector in which element J is obtained by using learners 1:J from the input list of learners.

Example: Mode="individual"

Data Types: char | string

Flag to run in parallel, specified as a numeric or logical 1 (true) or 0 (false). If you specify UseParallel=true, the resubEdge function executes for-loop iterations by using parfor. The loop runs in parallel when you have Parallel Computing Toolbox™.

Example: UseParallel=true

Data Types: logical

More About

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Classification Edge

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

Classification Margin

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.

Extended Capabilities

Version History

Introduced in R2011a

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