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Predict response of ensemble by resubstitution


Yfit = resubPredict(ens)
Yfit = resubPredict(ens,Name,Value)


Yfit = resubPredict(ens) returns the response ens predicts for the data ens.X. Yfit is the predictions of ens on the data that fitrensemble used to create ens.

Yfit = resubPredict(ens,Name,Value) predicts responses with additional options specified by one or more Name,Value pair arguments.

Input Arguments


A regression ensemble created with fitrensemble.

Name-Value 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.


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

Default: 1:NumTrained

Output Arguments


A vector of predicted responses to the training data, with ens.X elements.


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Find the resubstitution predictions of mileage from the carsmall data, and look at their mean-squared difference from the 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.

ens = fitrensemble(X,MPG,'Method','LSBoost','Learners','Tree');

Find the resubstitution predictions of MPG.

Yfit = resubPredict(ens);

Calculate the mean-squared difference of the resubstitution predictions from the training data.

MSE = mean((Yfit - ens.Y).^2)
MSE = 0.5836

Confirm that the result is the same as the result of resubLoss.

ans = 0.5836