Documentation

resubLoss

Regression error by resubstitution

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 Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

 'learners' Indices of weak learners in the ensemble ranging from 1 to 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'

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

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

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