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Out-of-bag classification margins of bagged classification ensemble



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, and perform computations in parallel.


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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

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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],UseParallel=true) specifies to use the first, second, third, and fifth learners in the ensemble in oobMargin, and to perform computations in parallel.

Indices of weak learners in the ensemble to use in oobMargin, 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

Flag to run in parallel, specified as a numeric or logical 1 (true) or 0 (false). If you specify UseParallel=true, the oobMargin 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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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 dataset, fitrensemble generates many bootstrap replicas of the dataset 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 dataset 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

Version History

Introduced in R2012b