# cv.tree: Cross-validation for Choosing Tree Complexity

## 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`

.

## 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`

.

## 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.

## Examples

# NOT RUN {
data(cpus, package="MASS")
cpus.ltr <- tree(log10(perf) ~ syct + mmin + mmax + cach
+ chmin + chmax, data=cpus)
cv.tree(cpus.ltr, , prune.tree)
# }