rt.prune:
Prune a tree-based model using the SE rule
Description
This function implements the SE post pruning rule described in the
CART book (Breiman et. al., 1984)
Usage
rt.prune(tree, se = 1, verbose = T, ...)
Arguments
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()
Value
A rpart object
Details
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.
References
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. (2010) Data Mining using R: learning with case studies,
CRC Press (ISBN: 9781439810187).
data(iris)
tree <- rpartXse(Species ~ ., iris)
tree
## A visual representation of the classification tree## Not run: # prettyTree(tree)# ## End(Not run)