bagging function

bagging method

bagging(form, data, ntrees, pruning, dselection, pruning_cp)

Arguments

• form: formula

• data: training data

• ntrees: ntrees

• pruning: model pruning method. A character vector. Currently, the following methods are supported:

  • mdsq: Margin-distance minimisation
  • bb: boosting based pruning
  • none: no pruning

• dselection: dynamic selection of the available models. Currently, the following methods are supported:

  • ola: Overall Local Accuracy
  • knora-e: K-nearest-oracles-eliminate
  • none: no dynamic selection. Majority voting is used.

• pruning_cp: The pruning cutpoint for the pruning method picked.

Examples

# splitting an example dataset into train/test:
train <- iris[1:(.7*nrow(iris)), ]
test <- iris[-c(1:(.7*nrow(iris))), ]
form <- Species ~.
# a user-defined bagging workflow
m <- bagging(form, iris, ntrees = 5, pruning = "bb", pruning_cp = .5, dselection = "ola")
preds <- predict(m, test)
# a standard bagging workflow with 5 trees (5 trees for examplification purposes):
m2 <- bagging(form, iris, ntrees = 5, pruning = "none", dselection = "none")
preds2 <- predict(m2, test)

See Also

baggedtrees for the implementation of the bagging model.