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Predict out-of-bag responses of bagged regression ensemble



Yfit = oobPredict(ens) returns a numeric column vector of predicted responses for the out-of-bag data in the bagged regression ensemble model ens. Yfit has size(ens.X,1) elements. You can find the indices of out-of-bag observations for weak learner L with the command


Yfit = oobPredict(ens,Name=Value) specifies additional options using one or more name-value arguments. For example, you can specify the indices of the weak learners to use for calculating the predicted responses, and perform computations in parallel.


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Compute the out-of-bag predictions for the carsmall data set. Display the first three terms of the fit.

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

load carsmall
X = [Displacement Horsepower Weight];

Train an ensemble of bagged regression trees.

ens = fitrensemble(X,MPG,'Method','Bag');

Find the out-of-bag predictions, and display the first three terms of the fit.

Yfit = oobPredict(ens);
Yfit(1:3) % First three terms
ans = 3×1


Input Arguments

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Bagged regression ensemble model, specified as a RegressionBaggedEnsemble model object trained 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.

Example: oobPredict(ens,Learners=[1 2 3 5],UseParallel=true) specifies to use the first, second, third, and fifth learners in the ensemble in oobPredict, and to perform computations in parallel.

Indices of weak learners in the ensemble to use in oobPredict, specified as a vector of positive integers in the range [1:ens.NumTrained]. By default, all learners are used.

Example: Learners=[1 2 4]

Data Types: single | double

Flag to run in parallel, specified as a numeric or logical 1 (true) or 0 (false). If you specify UseParallel=true, the oobPredict 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

More About

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Out of Bag

Bagging, which stands for “bootstrap aggregation”, is a type of ensemble learning. To bag a weak learner such as a decision tree on a dataset, fitrensemble generates many bootstrap replicas of the dataset and grows decision trees on these replicas. fitrensemble obtains each bootstrap replica by randomly selecting N observations out of N with replacement, where N is the dataset size. To find the predicted response of a trained ensemble, predict takes an average over predictions from individual trees.

Drawing N out of N observations with replacement omits on average 37% (1/e) of observations for each decision tree. These are "out-of-bag" observations. For each observation, oobLoss estimates the out-of-bag prediction by averaging over predictions from all trees in the ensemble for which this observation is out of bag. It then compares the computed prediction against the true response for this observation. It calculates the out-of-bag error by comparing the out-of-bag predicted responses against the true responses for all observations used for training. This out-of-bag average is an unbiased estimator of the true ensemble error.

Extended Capabilities

Version History

Introduced in R2012b