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resubLoss

Regression error by resubstitution

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Syntax

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

Description

L = resubLoss(ens) returns the resubstitution loss, meaning the mean squared error computed for the data that fitrensemble used to create ens.

L = resubLoss(ens,Name,Value) calculates loss with additional options specified by one or more Name,Value pair arguments. You can specify several name-value pair arguments in any order as Name1,Value1,…,NameN,ValueN.

Input Arguments

ens

A regression ensemble created with fitrensemble.

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.

learners

Indices of weak learners in the ensemble ranging from 1 to ens.NumTrained. resubLoss uses only these learners for calculating loss.

Default: 1:NumTrained

lossfun

Function handle for loss function, or 'mse', meaning mean squared error. 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, and W is the observation weights.

Default: 'mse'

mode

Character vector or string scalar representing the meaning of the output L:

  • 'ensemble' — L is a scalar value, the loss for the entire ensemble.

  • 'individual' — L is a vector with one element per trained learner.

  • 'cumulative' — L is a vector in which element J is obtained by using learners 1:J from the input list of learners.

Default: 'ensemble'

UseParallel

Indication to perform inference in parallel, specified as false (compute serially) or true (compute in parallel). Parallel computation requires Parallel Computing Toolbox™. Parallel inference can be faster than serial inference, especially for large datasets. Parallel computation is supported only for tree learners.

Default: false

Output Arguments

L

Loss, by default the mean squared error. L can be a vector, and can mean different things, depending on the name-value pair settings.

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

Extended Capabilities

See Also

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