oobLoss

R2026b

Out-of-bag error for bagged regression ensemble model

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Syntax

L = oobLoss(ens)
L = oobLoss(ens,Name=Value)

Description

L = oobLoss(ens) returns the mean squared error L for the out-of-bag data in the bagged regression ensemble model ens. The interpretation of L depends on the loss function (LossFun).

example

L = oobLoss(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 error, the aggregation level for the output, and the loss function.

Examples

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Open Live Script

Compute the out-of-bag error for the carsmall data.

Load the carsmall data set and select engine displacement, horsepower, and vehicle weight as predictors.

load carsmall
X = [Displacement Horsepower Weight];

Train an ensemble of bagged regression trees.

ens = fitrensemble(X,MPG,'Method','Bag');

Find the out-of-bag error.

L = oobLoss(ens)
L = 
16.9551

Input Arguments

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Bagged regression ensemble model, specified as a RegressionBaggedEnsemble model object trained with fitrensemble.

Name-Value Arguments

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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: oobLoss(ens,Learners=[1 2 3 5],UseParallel="auto") specifies to use the first, second, third, and fifth learners in the ensemble, and to perform computations in parallel.

Indices of the weak learners in the ensemble to use with oobLoss, 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

Loss function, specified as "mse" (mean squared error) or as a function handle. If you pass a function handle fun, oobLoss calls it as

fun(Y,Yfit,W)

where Y, Yfit, and W are numeric vectors of the same length.

  • Y is the observed response.

  • Yfit is the predicted response.

  • W is the observation weights.

The returned value of fun(Y,Yfit,W) must be a scalar.

Example: LossFun="mse"

Example: LossFun=@Lossfun

Data Types: char | string | function_handle

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

ValueDescription
"ensemble"The output is a scalar value 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

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 (1).

Example: UseParallel="auto"

Data Types: char | string

More About

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Extended Capabilities

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Version History

Introduced in R2012b

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See Also

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