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resume

Resume training of regression ensemble model

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Syntax

ens1 = resume(ens,nlearn)
ens1 = resume(ens,nlearn,Name=Value)

Description

ens1 = resume(ens,nlearn) continues training with the same options used to train ens, except parallel training options and printout frequency, for nlearn more training cycles. The function returns a new regression ensemble model ens1.

example

ens1 = resume(ens,nlearn,Name=Value) 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.

Examples

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Open Live Script

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

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Regression ensemble model, specified as a RegressionEnsemble or RegressionBaggedEnsemble model object trained with fitrensemble.

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.

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 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 NameValueDefault
UseParallel

Set this value to true to compute in parallel. 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 Method="Bag".

false
UseSubstreams

Set this value to true to perform computations in a reproducible manner.

To compute reproducibly, set Streams to a type that allows substreams: "mlfg6331_64" or "mrg32k3a".

false
StreamsSpecify 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

Version History

Introduced in R2011a

See Also

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