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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.

cv.tree(object, rand, FUN = prune.tree, K = 10, ...)

`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`.

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.

B. D. Ripley

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

Classification and Regression Trees

v1.0-40

GPL-2 | GPL-3

Authors

Brian Ripley [aut, cre]

Initial release

2019-03-01