oobLoss
Out-of-bag regression error
Syntax
L = oobLoss(ens)
L = oobLoss(ens,Name,Value)
Description
returns
the mean squared error for L
= oobLoss(ens
)ens
computed for out-of-bag
data.
computes
error with additional options specified by one or more L
= oobLoss(ens
,Name,Value
)Name,Value
pair
arguments. You can specify several name-value pair arguments in any
order as Name1,Value1,…,NameN,ValueN
.
Input Arguments
|
A regression bagged ensemble, constructed with |
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 elementJ
is obtained by using learners1: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
|
Mean squared error of the out-of-bag observations, a scalar. |
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).
See Also
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