oobMargin

R2026b

Out-of-bag classification margins for bagged classification ensemble

Syntax

m = oobMargin(ens)

m = oobMargin(ens,Name=Value)

Description

m = oobMargin(ens) returns the classification margins for the out-of-bag data in the bagged classification ensemble model ens.

m = oobMargin(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 margins.

Examples

Find Out-of-Bag Classification Margins

Find the out-of-bag margins for a bagged ensemble from the Fisher iris data.

Load the sample data set.

load fisheriris

Train an ensemble of bagged classification trees.

ens = fitcensemble(meas,species,'Method','Bag');

Find the number of out-of-bag margins that are equal to 1.

margin = oobMargin(ens);
sum(margin == 1)

ans = 
109

Input Arguments

ens — Bagged classification ensemble model

ClassificationBaggedEnsemble model object

Bagged classification ensemble model, specified as a ClassificationBaggedEnsemble 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: oobMargin(ens,Learners=[1 2 3 5]) specifies to use the first, second, third, and fifth learners in the ensemble ens.

Learners — Indices of weak learners

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

Indices of the weak learners in the ensemble to use with oobMargin, specified as a vector of positive integers in the range [1:ens.NumTrained]. By default, the function uses all learners.

Example: Learners=[1 2 4]

Data Types: single | double

UseParallel — Option to perform computations in parallel

"off" (default) | "auto" | "on"

Option to perform computations in parallel using a parallel pool of workers, specified as one of these values:

• "off" — Run in serial on the MATLAB^® client.

• "auto" — Use a parallel pool if one is open or if MATLAB can automatically create one. If a parallel pool is not available, run in serial on the MATLAB client.

• "on" — Use a parallel pool if one is open or if MATLAB can automatically create one. If a parallel pool is not available, throw an error.

If you do not have a parallel pool open and automatic pool creation is enabled, MATLAB opens a pool using the default cluster profile. To use a parallel pool to run computations in MATLAB, you must have Parallel Computing Toolbox™. For more information, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).

Before R2026b: To run in parallel, set UseParallel to true.

Example: UseParallel="auto"

Data Types: char | string

More About

Out of Bag

Bagging, which stands for “bootstrap aggregation”, is a type of ensemble learning. To bag a weak learner such as a decision tree on a data set, fitrensemble generates many bootstrap replicas of the data set and grows decision trees on these replicas. fitrensemble obtains each bootstrap replica by randomly selecting N observations out of N with replacement, where N is the data set size. To find the predicted response of a trained ensemble, predict takes an average over predictions from individual trees.

Drawing N out of N observations with replacement omits on average 37% (1/e) of observations for each decision tree. These are "out-of-bag" observations. For each observation, oobLoss estimates the out-of-bag prediction by averaging over predictions from all trees in the ensemble for which this observation is out of bag. It then compares the computed prediction against the true response for this observation. It calculates the out-of-bag error by comparing the out-of-bag predicted responses against the true responses for all observations used for training. This out-of-bag average is an unbiased estimator of the true ensemble error.

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.

Extended Capabilities

Automatic Parallel Support

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

The oobMargin function has automatic parallel support. To run computations in parallel, set the UseParallel argument to "on" or "auto".

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

Version History

Introduced in R2012b

R2026b: Enhanced control over parallel execution with UseParallel argument

The UseParallel name-value argument now accepts "off", "auto", or "on" values instead of true or false. This change gives you more control over when to use a parallel pool for parallel execution. Specifying the UseParallel name-value argument as true or false is not recommended.

This table shows how to update your code depending on your goal.

Goal	Not Recommended	Recommended
Write code that runs on the MATLAB client.	UseParallel=false	UseParallel="off"
Write portable code that runs on a parallel pool and, if a pool is not available, runs on the MATLAB client.	UseParallel=true	UseParallel="auto"
Write code that runs on a parallel pool and errors if a pool is not available.	N/A	UseParallel="on"

There are no plans to remove support for the true or false values.