Resume training ensemble
ens1 = resume(ens,nlearn)
ens1 = resume(ens,nlearn,Name,Value)
A cross-validated regression 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 regression ensemble ens, augmented with additional training.
Train a regression ensemble for 50 cycles, and cross validate it. Examine the cross-validation error. Then train for 50 more cycles and examine the new cross-validation error.
load carsmall X = [Displacement Horsepower Weight]; ens = fitensemble(X,MPG,'LSBoost',50,'Tree'); cvens = crossval(ens); L = kfoldLoss(cvens) L = 25.6573 cvens = resume(cvens,50); L = kfoldLoss(cvens) L = 26.7563
The additional training did not improve the cross-validation error.