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oobPredict

Class: RegressionBaggedEnsemble

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 fitensemble.

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 single 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. oobLoss uses only these learners for calculating loss.

Default: 1:NumTrained

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)

Definitions

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, fitensemble generates many bootstrap replicas of the dataset and grows decision trees on these replicas. fitensemble 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 take 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.

Examples

Compute out-of-bag predictions for the carsmall data. Look at the first three terms of the fit:

load carsmall
X = [Displacement Horsepower Weight];
ens = fitensemble(X,MPG,'bag',100,'Tree',...
    'type','regression');
Yfit = oobPredict(ens);
Yfit(1:3) % first three terms

ans =
   15.7964
   14.7162
   14.8062

See Also

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