resume
Resume training of regression ensemble model
Description
specifies additional options using one or more name-value arguments. For example,
you can specify the printout frequency, and set options for computing in
parallel.ens1
= resume(ens
,nlearn
,Name=Value
)
Examples
Train Regression Ensemble for Additional Cycles
Train a regression ensemble for 50 cycles, and compare the resubstitution error obtained after training the ensemble for more cycles.
Load the carsmall
data set and select displacement, horsepower, and vehicle weight as predictors.
load carsmall
X = [Displacement Horsepower Weight];
Train a regression ensemble for 50 cycles and examine the resubstitution error.
ens = fitrensemble(X,MPG,NumLearningCycles=50); L = resubLoss(ens)
L = 0.5563
Train for 50 more cycles and examine the new resubstitution error.
ens = resume(ens,50); L = resubLoss(ens)
L = 0.3463
The resubstitution error is lower in the new ensemble than in the original.
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
.
nlearn
— Number of additional training cycles
positive integer
Number of additional training cycles for ens
, specified as a positive
integer.
Data Types: double
| single
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: resume(ens,10,NPrint=5,Options=statset(UseParallel=true))
specifies to train ens
for an additional 10 cycles, display a
message to the command line every time resume
finishes
training 5 weak learners, and to perform computations in parallel.
NPrint
— Printout frequency
"off"
(default) | positive integer
Printout frequency, specified as a positive integer
m
or "off"
.
resume
displays a message to the command
line every time it finishes training m
weak learners.
If you specify "off"
,
resume
does not display a message when it
completes training weak learners.
Example: NPrint=5
Data Types: single
| double
| char
| string
Options
— Options for computing in parallel and setting random number streams
structure
Options for computing in parallel and setting random number streams, specified as a
structure. Create the Options
structure using statset
.
Note
You need Parallel Computing Toolbox™ to run computations in parallel.
You can use the same parallel options for resume
as you used for the
original training. Use the Options
argument to change the parallel options,
as needed. This table describes the option fields and their values.
Field Name | Value | Default |
---|---|---|
UseParallel | Set this value to | false |
UseSubstreams | Set this value to To compute reproducibly, set
| false |
Streams | Specify this value as a RandStream object or cell array of such objects. Use a single object
except when the UseParallel value is true and
the UseSubstreams value is false . In that case,
use a cell array that has the same size as the parallel pool. | If you do not specify Streams ,
resume uses the default stream or streams. |
For dual-core systems and above, resume
parallelizes training
using Intel® Threading Building Blocks (TBB). Therefore, setting
UseParallel
to true
might not provide a significant
increase in speed on a single computer. For details on Intel TBB, see https://www.intel.com/content/www/us/en/developer/tools/oneapi/onetbb.html.
Example: Options=statset(UseParallel=true)
Data Types: struct
Extended Capabilities
Automatic Parallel Support
Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.
resume
supports parallel training
using the 'Options'
name-value argument. Create options using statset
, such as options = statset('UseParallel',true)
.
Parallel ensemble training requires you to set the 'Method'
name-value
argument to 'Bag'
. Parallel training is available only for tree learners, the
default type for 'Bag'
.
GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.
This function fully supports GPU arrays. For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).
Version History
Introduced in R2011a
See Also
fitrensemble
| RegressionEnsemble
| RegressionBaggedEnsemble
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