resubLoss - Resubstitution loss for regression ensemble model - MATLAB (original) (raw)

Resubstitution loss for regression ensemble model

Syntax

Description

`L` = resubLoss([ens](#bst9qk8-1%5Fsep%5Fmw%5Fdb6ecd40-08eb-4956-a4a2-5fc22e0fa24f)) returns the resubstitution loss computed for the data used by fitrensemble to create ens. By default,resubLoss uses the mean squared error to computeL.

example

`L` = resubLoss([ens](#bst9qk8-1%5Fsep%5Fmw%5Fdb6ecd40-08eb-4956-a4a2-5fc22e0fa24f),[Name=Value](#namevaluepairarguments)) 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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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)

Input Arguments

Name-Value Arguments

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Specify optional pairs of arguments asName1=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=true) 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 withresubLoss, 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

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

Data Types: char | string

Flag to run in parallel, specified as a numeric or logical1 (true) or 0 (false). If you specify UseParallel=true, theresubLoss 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

Extended Capabilities

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To run in parallel, set the UseParallel name-value argument totrue in the call to this function.

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

You cannot use UseParallel with GPU arrays.

Usage notes and limitations:

• You cannot use UseParallel with GPU arrays.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Version History

Introduced in R2011a