L = oobLoss(ens)
L = oobLoss(ens,Name,Value)
returns
the mean squared error for L
= oobLoss(ens
)ens
computed for outofbag
data.
computes
error with additional options specified by one or more L
= oobLoss(ens
,Name,Value
)Name,Value
pair
arguments. You can specify several namevalue pair arguments in any
order as Name1,Value1,…,NameN,ValueN
.

A regression bagged ensemble, constructed with 
Specify optional commaseparated 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
.

Indices of weak learners in the ensemble ranging from Default: 

Function handle for loss function, or the string FUN(Y,Yfit,W) where Default: 

String representing the meaning of the output
Default: 

Mean squared error of the outofbag observations, a scalar. 
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 "outofbag" observations.
For each observation, oobLoss
estimates the outofbag
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 outofbag error by comparing the outofbag predicted responses
against the true responses for all observations used for training.
This outofbag average is an unbiased estimator of the true ensemble
error.
Compute the outofbag error for the carsmall
data:
load carsmall X = [Displacement Horsepower Weight]; ens = fitensemble(X,MPG,'bag',100,'Tree',... 'type','regression'); L = oobLoss(ens) L = 17.0665