resubLoss

R2026b

Resubstitution loss for regression ensemble model

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Syntax

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

Description

L = resubLoss(ens) returns the resubstitution loss computed for the data used by fitrensemble to create ens. By default, resubLoss uses the mean squared error to compute L.

example

L = resubLoss(ens,Name=Value) specifies additional options using one or more name-value arguments. For example, you can specify the loss function, the aggregation level for output, and whether to perform computations in parallel.

Examples

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Open Live Script

Find the mean-squared difference between resubstitution predictions and training data.

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

load carsmall
X = [Horsepower Weight];

Train an ensemble of regression trees, and find the mean-squared difference of predictions from the training data.

ens = fitrensemble(X,MPG);
MSE = resubLoss(ens) 
MSE = 
0.5836

Input Arguments

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Regression ensemble model, specified as a RegressionEnsemble or 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: resubLoss(ens,Learners=[1 2 4],UseParallel="auto") specifies to use the first, second, and fourth weak learners in the ensemble, and to perform computations in parallel.

Indices of the weak learners in the ensemble to use with resubLoss, 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, resubLoss 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.

Example: UseParallel="auto"

Data Types: char | string

Extended Capabilities

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Version History

Introduced in R2011a

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See Also

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