mlr (version 2.7)

makeStackedLearner: Create a stacked learner object.

Description

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:

[object Object],[object Object],[object Object],[object Object],[object Object]

Usage

makeStackedLearner(base.learners, super.learner = NULL, predict.type = NULL,
  method = "stack.nocv", use.feat = FALSE, resampling = NULL,
  parset = list())

Examples

# 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)
  res = predict(tmp, tsk)
  
  # 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)
  res = predict(tmp, tsk)