oobLoss
Out-of-bag error for bagged regression ensemble model
Description
specifies additional options using one or more name-value arguments. For example,
you can specify the indices of the weak learners to use for calculating the error,
the aggregation level for the output, and the loss function.L
= oobLoss(ens
,Name=Value
)
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
Input Arguments
ens
— Bagged regression ensemble model
RegressionBaggedEnsemble
model object
Bagged regression ensemble model, specified as a RegressionBaggedEnsemble
model object trained 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.
Example: oobLoss(ens,Learners=[1 2 3 5],UseParallel=true)
specifies to use the first, second, third, and fifth learners in the ensemble, and
to perform computations in parallel.
Learners
— Indices of weak learners
[1:ens.NumTrained]
(default) | vector of positive integers
Indices of the weak learners in the ensemble to use with
oobLoss
, specified as a
vector of positive integers in the range
[1:ens.NumTrained
]. By default,
the function uses all learners.
Example: Learners=[1 2 4]
Data Types: single
| double
LossFun
— Loss function
"mse"
(default) | function handle
Loss function, specified as "mse"
(mean squared error) or as a
function handle. 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.W
is the observation weights.
The returned value of fun(Y,Yfit,W)
must be a scalar.
Example: LossFun="mse"
Example: LossFun=@
Lossfun
Data Types: char
| string
| function_handle
Mode
— Aggregation level for output
"ensemble"
(default) | "individual"
| "cumulative"
Aggregation level for the output, specified as "ensemble"
,
"individual"
, or "cumulative"
.
Value | Description |
---|---|
"ensemble" | The output is a scalar value, the loss for the entire ensemble. |
"individual" | The output is a vector with one element per trained learner. |
"cumulative" | The output is a vector in which element J is
obtained by using learners 1:J from the input
list of learners. |
Example: Mode="individual"
Data Types: char
| string
UseParallel
— Flag to run in parallel
false
or
0
(default) | true
or 1
Flag to run in parallel, specified as a numeric or logical 1
(true
) or 0 (false
). If you specify
UseParallel=true
, the oobLoss
function executes
for
-loop iterations by using parfor
. The loop runs in parallel when you have Parallel Computing Toolbox™.
Example: UseParallel=true
Data Types: logical
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).
Version History
Introduced in R2012b
See Also
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