resubMargin

Classification margins by resubstitution

Syntax

margin = resubMargin(ens) margin = resubMargin(ens,Name,Value)

Description

margin = resubMargin(ens) returns the classification margin obtained by ens on its training data.

margin = resubMargin(ens,Name,Value) calculates margins with additional options specified by one or more Name,Value pair arguments.

Input Arguments

ens

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.

'learners'

Indices of weak learners in the ensemble ranging from 1 to NumTrained. resubMargin uses only these learners for calculating margin.

Default: 1:NumTrained

Output Arguments

margin

A numeric column-vector of length size(ens.X,1) containing the classification margins.

Examples

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Compute Resubstitution Margins for Classification Ensemble

Open Live Script

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

Load the Fisher iris 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 margins.

margin = resubMargin(ens);
[min(margin) mean(margin) max(margin)]

ans = 1×3

   -0.5674    3.2486    4.6245

More About

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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.

Documentation