resubEdge

Classification edge by resubstitution

Syntax

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


Description

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

ens	A classification ensemble created 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.

learners	Indices of weak learners in the ensemble ranging from 1 to ens.NumTrained. resubEdge uses only these learners for calculating loss. Default: 1:NumTrained
mode	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'
UseParallel	Indication to perform inference in parallel, specified as false (compute serially) or true (compute in parallel). Parallel computation requires Parallel Computing Toolbox™. Parallel inference can be faster than serial inference, especially for large datasets. Parallel computation is supported only for tree learners. Default: false

Output Arguments

edge	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.

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

More About

Edge

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

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

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 can return a different value.

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

If you want the software to handle the cost matrix, prior probabilities, and observation weights 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.