Cross-validation for Choosing Tree Complexity
Usage
cv.tree(object, rand, FUN=prune.tree, K=10, ...)
Arguments
object
|
An object of class "tree" .
|
rand
|
Optionally an integer vector of the length the number of
cases used to create object , assigning the cases to different
groups for cross-validation.
|
FUN
|
The function to do the pruning.
|
K
|
The number of folds of the cross-validation.
|
...
|
Additional arguments to FUN .
|
Description
Runs a K-fold cross-validation experiment to find the deviance or
number of misclassifications as a function of the cost-complexity
parameter k
.Value
A copy of FUN
applied to object
, with component
dev
replaced by the cross-validated results from the
sum of the dev
components of each fit.Author(s)
B.D. RipleySee Also
tree
, prune.tree
Examples
library(MASS)
data(cpus)
cpus.ltr <- tree(log10(perf) ~ syct + mmin + mmax + cach
+ chmin + chmax, cpus)
cv.tree(cpus.ltr, , prune.tree)