R: Cross-validation for Choosing Tree Complexity

cv.tree {tree} | R Documentation

## 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.

### Author(s)

B. D. Ripley

### See Also

`tree`, `prune.tree`

### Examples

    data(cpus, package="MASS")
    cpus.ltr <- tree(log10(perf) ~ syct + mmin + mmax + cach
         + chmin + chmax, data=cpus)
    cv.tree(cpus.ltr, , prune.tree)

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