mlr (version 2.9)

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:

Usage

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

Arguments

base.learners

[(list of) Learner] A list of learners created with makeLearner.

super.learner

[Learner | character(1)] The super learner that makes the final prediction based on the base learners. If you pass a string, the super learner will be created via makeLearner. Not used for method = 'average'. Default is NULL.

predict.type

[character(1)] Sets the type of the final prediction for method = 'average'. For other methods, the predict type should be set within super.learner. If the type of the base learner prediction, which is set up within base.learners, is

method

[character(1)] “average” for averaging the predictions of the base learners, “stack.nocv” for building a super learner using the predictions of the base learners, “stack.cv” for building a super learner using crossvalidated predictions of the base learners. “hill.climb” for averaging the predictions of the base learners, with the weights learned from hill climbing algorithm and “compress” for compressing the model to mimic the predictions of a collection of base learners while speeding up the predictions and reducing the size of the model. Default is “stack.nocv”,

use.feat

[logical(1)] Whether the original features should also be passed to the super learner. Not used for method = 'average'. Default is FALSE.

resampling

[ResampleDesc] Resampling strategy for method = 'stack.cv'. Currently only CV is allowed for resampling. The default NULL uses 5-fold CV.

parset

the parameters for hill.climb method, including

the parameters for compress method, including

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)