# cv.tree

##### Cross-validation for Choosing Tree Complexity

Runs a K-fold cross-validation experiment to find the deviance or
number of misclassifications as a function of the cost-complexity
parameter `k`

.

- Keywords
- tree

##### 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.

##### See Also

##### 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)
# }
```

*Documentation reproduced from package tree, version 1.0-40, License: GPL-2 | GPL-3*

### Community examples

**jshusko**at Apr 14, 2020 tree v1.0-40

```r library(ISLR) library(dplyr) library(tree) library(tibble) carseats <- as_tibble(Carseats) %>% mutate(High=as.factor(Sales>8)) train <- sample(1:nrow(carseats), 200) tree.carseats <- tree(High~.-Sales, carseats, subset=train) cv.carseats <- cv.tree(tree.carseats,FUN=prune.misclass) cv.carseats ```