resume

Resume training ensemble

Syntax

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


Description

ens1 = resume(ens,nlearn) trains ens for nlearn more cycles. resume uses the same training options fitrensemble used to create ens, except for parallel training options. If you want to resume training in parallel, pass the 'Options' name-value pair.

ens1 = resume(ens,nlearn,Name,Value) trains ens with additional options specified by one or more Name,Value pair arguments.

Input Arguments

ens	A regression ensemble, created with fitrensemble.
nlearn	A positive integer, the number of cycles for additional training of ens.

Name-Value Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside quotes. You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.

NPrint

Printout frequency, a positive integer scalar or 'off' (no printouts). When NPrint is a positive integer, displays a message to the command line after training NPrint weak learners. Tip For fastest training of some boosted decision trees, set NPrint to the default value 'off'. This tip holds when the classification Method is 'AdaBoostM1', 'AdaBoostM2', 'GentleBoost', or 'LogitBoost', or when the regression Method is 'LSBoost'. Default: 'off'

Options

Options for computing in parallel and setting random numbers, specified as a structure. Create the Options structure with statset. Note You need Parallel Computing Toolbox™ to compute in parallel. You can use the same parallel options for resume as you used for the original training. However, you can change the parallel options as needed. This table lists the option fields and their values. Field Name Value Default 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 'Bag'. false UseSubstreams Set this value to true to run computations in parallel in a reproducible manner. To compute reproducibly, set Streams to a type that allows substreams: 'mlfg6331_64' or 'mrg32k3a'. 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, then resume uses the default stream or streams. For dual-core systems and above, resume parallelizes training using Intel® Threading Building Blocks (TBB). Therefore, specifying the UseParallel option as true might not provide a significant speedup on a single computer. For details on Intel TBB, see https://software.intel.com/content/www/us/en/develop/tools/oneapi/components/onetbb.html. Example: 'Options',statset('UseParallel',true)

Output Arguments

ens1	The regression ensemble ens, augmented with additional training.

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.

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'.