A stacked learner uses predictions of several base learners and fits a super learner using these predictions as features in order to predict the outcome. The following stacking methods are available:
average
Averaging of base learner predictions without weights.
stack.nocv
Fits the super learner, where insample predictions of
the base learners are used.
stack.cv
Fits the super learner, where the base learner predictions
are computed by crossvalidated predictions (the resampling strategy can be
set via the resampling
argument).
hill.climb
Select a subset of base learner predictions by hill
climbing algorithm.
compress
Train a neural network to compress the model from a
collection of base learners.
makeStackedLearner( base.learners, super.learner = NULL, predict.type = NULL, method = "stack.nocv", use.feat = FALSE, resampling = NULL, parset = list() )
base.learners  ((list of) Learner) 

super.learner  (Learner  character(1)) 
predict.type  (

method  ( 
use.feat  ( 
resampling  (ResampleDesc) 
parset  the parameters for
the parameters for

# Classification data(iris) tsk = makeClassifTask(data = iris, target = "Species") base = c("classif.rpart", "classif.lda", "classif.svm") lrns = lapply(base, makeLearner) lrns = lapply(lrns, setPredictType, "prob") m = makeStackedLearner(base.learners = lrns, predict.type = "prob", method = "hill.climb") tmp = train(m, tsk)#> Error: Please use column names for `x`#> Error in predict(tmp, tsk): object 'tmp' not found# Regression data(BostonHousing, package = "mlbench") tsk = makeRegrTask(data = BostonHousing, target = "medv") base = c("regr.rpart", "regr.svm") lrns = lapply(base, makeLearner) m = makeStackedLearner(base.learners = lrns, predict.type = "response", method = "compress") tmp = train(m, tsk)#> Error: Please use column names for `x`#> Error in predict(tmp, tsk): object 'tmp' not found