oobLoss

Out-of-bag regression error

Syntax

L = oobLoss(ens)
L = oobLoss(ens,Name,Value)


Description

L = oobLoss(ens) returns the mean squared error for ens computed for out-of-bag data.

L = oobLoss(ens,Name,Value) computes error 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 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

[1:ens.NumTrained] (default) | vector of positive integers

Indices of weak learners in the ensemble to use in oobLoss, 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

lossfun —

Function handle for loss function, or 'mse', meaning mean squared error. If you pass a function handle fun, oobLoss 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.

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.

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.

Output Arguments

L	Mean squared error of the out-of-bag observations, a scalar. L can be a vector, or can represent a different quantity, depending on the name-value settings.

Examples

Find Out-of-Bag Regression Error

Compute the out-of-bag error for the carsmall data.

Load the carsmall data set and select engine 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 error.

L = oobLoss(ens)

L = 16.9551

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).