Out-of-bag regression error
L = oobLoss(ens)
L = oobLoss(ens,Name,Value)
error with additional options specified by one or more
L = oobLoss(
arguments. You can specify several name-value pair arguments in any
A regression bagged ensemble, constructed with
Specify optional pairs of arguments as
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
ens.NumTrained]. By default, all learners are used.
Learners=[1 2 4]
Function handle for loss function, or
meaning mean squared error. If you pass a function handle
numeric vectors of the same length.
Y is the observed
Yfit is the predicted response, and
the observation weights.
Character vector or string scalar representing the meaning of the output
Lis a scalar value, the loss for the entire ensemble.
Lis a vector with one element per trained learner.
Lis a vector in which element
Jis obtained by using learners
1:Jfrom the input list of learners.
Indication to perform inference in parallel, specified as
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.
Mean squared error of the out-of-bag observations, a scalar.
Find Out-of-Bag Regression Error
Compute the out-of-bag error for 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
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
predict takes an average over predictions from
N out of
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
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).