resubLoss
Resubstitution loss for regression ensemble model
Description
returns the resubstitution loss computed for the data used by L
= resubLoss(ens
)fitrensemble
to create ens
. By default,
resubLoss
uses the mean squared error to compute
L
.
specifies additional options using one or more name-value arguments. For example,
you can specify the loss function, the aggregation level for output, and whether to
perform computations in parallel.L
= resubLoss(ens
,Name=Value
)
Examples
Estimate Resubstitution Loss
Find the mean-squared difference between resubstitution predictions and training data.
Load the carsmall
data set and select horsepower and vehicle weight as predictors.
load carsmall
X = [Horsepower Weight];
Train an ensemble of regression trees, and find the mean-squared difference of predictions from the training data.
ens = fitrensemble(X,MPG); MSE = resubLoss(ens)
MSE = 0.5836
Input Arguments
ens
— Regression ensemble model
RegressionEnsemble
model object | RegressionBaggedEnsemble
model object
Regression ensemble model, specified as a RegressionEnsemble
or 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: resubLoss(ens,Learners=[1 2 4],UseParallel=true)
specifies to use the first, second, and fourth weak 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
resubLoss
, 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
, resubLoss
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
resubLoss
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
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).
You cannot use UseParallel
with GPU arrays.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
Usage notes and limitations:
You cannot use
UseParallel
with GPU arrays.
For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2011a
See Also
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