bagging: bagging method
In autoBagging: Learning to Rank Bagging Workflows with Metalearning

Description Usage Arguments See Also Examples

bagging method

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

`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.

baggedtrees for the implementation of the bagging model.

# 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)