rt.prune: Prune a tree-based model using the SE rule
In DMwR2: Functions and Data for the Second Edition of "Data Mining with R"

Description Usage Arguments Details Value Author(s) References See Also Examples

This function implements the SE post pruning rule described in the CART book (Breiman et. al., 1984)

1	rt.prune(tree, se = 1, verbose = T, ...)

`tree`	An `rpart` object
`se`	The value of the SE threshold (defaulting to 1)
`verbose`	The level of verbosity (defaulting to T)
`...`	Any other arguments passed to the function `prune.rpart()`

The x-SE rule for tree post-pruning is based on the cross-validation estimates of the error of the sub-trees of the initially grown tree, together with the standard errors of these estimates. These values are used to select the final tree model. Namely, the selected tree is the smallest tree with estimated error less than the B+x*SE, where B is the lowest estimate of error and SE is the standard error of this B estimate.

A rpart object

Luis Torgo ltorgo@dcc.fc.up.pt

Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and regression trees. Statistics/Probability Series. Wadsworth & Brooks/Cole Advanced Books & Software.

Torgo, L. (2016) Data Mining using R: learning with case studies, second edition, Chapman & Hall/CRC (ISBN-13: 978-1482234893).

http://ltorgo.github.io/DMwR2

rt.prune, rpart, prune.rpart

data(iris)
tree <- rpartXse(Species ~ ., iris)
tree

## A visual representation of the classification tree
## Not run: 
prettyTree(tree)

## End(Not run)