oobPredict

Predict out-of-bag response of ensemble

Syntax

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


Description

Yfit = oobPredict(ens) returns the predicted responses for the out-of-bag data in ens.

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

Input Arguments

ens	A regression bagged ensemble, constructed 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. oobPredict uses only these learners for calculating loss. Default: 1:NumTrained
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

Yfit	A vector of predicted responses for out-of-bag data. Yfit has size(ens.X,1) elements. You can find the indices of out-of-bag observations for weak learner L with the command ~ens.UseObsForLearner(:,L)

Examples

Find Out-of-Bag Response of Regression Ensemble

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

   15.5200
   14.5558
   15.0231

More About

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

Automatic Parallel Support

Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.

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

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