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Resume training of classification ensemble model



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 classification ensemble model ens1.

ens1 = resume(___,Name=Value) specifies options using one or more name-value arguments. For example, you can track the number of weak learners as they are trained, and perform computations in parallel.


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Train a classification ensemble for three cycles, and compare the resubstitution error obtained after training the ensemble for more cycles.

Load the ionosphere data set.

load ionosphere

Train a classification ensemble for three cycles and examine the resubstitution error.

ens = fitcensemble(X,Y,Method="GentleBoost",NumLearningCycles=3);
L = resubLoss(ens)
L = 0.0085

Train for three more cycles and examine the new resubstitution error.

ens1 = resume(ens,3);
L = resubLoss(ens1)
L = 0

The resubstitution error is much lower in the new ensemble than the original.

Input Arguments

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Classification ensemble model, specified as a ClassificationEnsemble model object trained with fitcensemble.


You cannot resume training when ens is a model trained with these options set for fitcensemble: Method="Subspace" and NumLearningCycles="AllPredictorCombinations".

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.


For the fastest training of some boosted decision trees, when the classification Method is "AdaBoostM1", "AdaBoostM2", "GentleBoost", or "LogitBoost", set Nprint to "off" (the default value).

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.


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

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".


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".

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

Example: Options=statset(UseParallel=true)

Data Types: struct

Extended Capabilities

Version History

Introduced in R2011a