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:
Averaging of base learner predictions without weights.
Fits the super learner, where insample predictions of the base learners are used.
Fits the super learner, where the base learner predictions are computed by crossvalidated predictions (the resampling strategy can be set via the `resampling` argument).
Select a subset of base learner predictions by hill climbing algorithm.
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  [ 
predict.type  (`character(1)`)

method  (`character(1)`) 
use.feat  (`logical(1)`) 
resampling  ([ResampleDesc]) 
parset  the parameters for `hill.climb` method, including
the parameters for `compress` method, including

# 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