Resume training learners on cross-validation folds
ens1 = resume(ens,nlearn)
ens1 = resume(ens,nlearn,Name,Value)
A cross-validated classification ensemble. ens is the result of either:
A positive integer, the number of cycles for additional training of ens.
Specify optional comma-separated pairs of Name,Value arguments. Name is the argument name and Value is the corresponding value. Name must appear inside single quotes (' '). You can specify several name and value pair arguments in any order as Name1,Value1,...,NameN,ValueN.
Printout frequency, a positive integer scalar or 'off' (no printouts). Returns to the command line the number of weak learners trained so far. Useful when you train ensembles with many learners on large data sets.
The cross-validated classification ensemble ens, augmented with additional training.
Train a partitioned classification ensemble for 10 cycles. Examine the error. Then train for 10 more cycles and examine the new error.
load ionosphere cvens = fitensemble(X,Y,'GentleBoost',10,'Tree',... 'crossval','on'); L = kfoldLoss(cvens) L = 0.0883 cvens = resume(cvens,10); L = kfoldLoss(cvens) L = 0.0769
The ensemble has less cross-validation error after training for ten more cycles.