resubEdge

Resubstitution classification edge for classification ensemble model

Syntax

edge = resubEdge(ens)

edge = resubEdge(ens,Name=Value)

Description

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.

Examples

Find Classification Edge by Resubstitution of Training Data

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

ens — Classification ensemble model

ClassificationEnsemble model object

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.

Learners — Indices of weak learners

[1:ens.NumTrained] (default) | vector of positive integers

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

Mode — Aggregation level for output

"ensemble" (default) | "individual" | "cumulative"

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

Value	Description
"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

UseParallel — Flag to run in parallel

false or 0 (default) | true or 1

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

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

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

To run in parallel, set the UseParallel name-value argument to true in the call to this function.

For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

You cannot use UseParallel with GPU arrays.

GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• You cannot use UseParallel with GPU arrays.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2011a

R2023b: Observations with missing predictor values are used in resubstitution and cross-validation computations

Starting in R2023b, the following classification model object functions use observations with missing predictor values as part of resubstitution ("resub") and cross-validation ("kfold") computations for classification edges, losses, margins, and predictions.

Model Type	Model Objects	Object Functions
Discriminant analysis classification model	ClassificationDiscriminant	resubEdge, resubLoss, resubMargin, resubPredict
	ClassificationPartitionedModel	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
Ensemble of discriminant analysis learners for classification	ClassificationEnsemble	resubEdge, resubLoss, resubMargin, resubPredict
	ClassificationPartitionedEnsemble	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
Gaussian kernel classification model	ClassificationPartitionedKernel	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
	ClassificationPartitionedKernelECOC	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
Linear classification model	ClassificationPartitionedLinear	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
	ClassificationPartitionedLinearECOC	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
Neural network classification model	ClassificationNeuralNetwork	resubEdge, resubLoss, resubMargin, resubPredict
	ClassificationPartitionedModel	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict
Support vector machine (SVM) classification model	ClassificationSVM	resubEdge, resubLoss, resubMargin, resubPredict
	ClassificationPartitionedModel	kfoldEdge, kfoldLoss, kfoldMargin, kfoldPredict

In previous releases, the software omitted observations with missing predictor values from the resubstitution and cross-validation computations.

R2022a: resubEdge returns a different value for a model with a nondefault cost matrix

If you specify a nondefault cost matrix when you train the input model object, the resubEdge function returns a different value compared to previous releases.

The resubEdge function uses the observation weights stored in the W property. The way the function uses the W property value has not changed. However, the property value stored in the input model object has changed for a model with a nondefault cost matrix, so the function might return a different value.

For details about the property value changes, see Cost property stores the user-specified cost matrix.

If you want the software to handle the cost matrix, prior probabilities, and observation weights in the same way as in previous releases, adjust the prior probabilities and observation weights for the nondefault cost matrix, as described in Adjust Prior Probabilities and Observation Weights for Misclassification Cost Matrix. Then, when you train a classification model, specify the adjusted prior probabilities and observation weights by using the Prior and Weights name-value arguments, respectively, and use the default cost matrix.