# prune.tree

##### Cost-complexity Pruning of Tree Object

Determines a nested sequence of subtrees of the supplied tree by recursively “snipping” off the least important splits.

- Keywords
- tree

##### Usage

```
prune.tree(tree, k = NULL, best = NULL, newdata, nwts,
method = c("deviance", "misclass"), loss, eps = 1e-3)
```prune.misclass(tree, k = NULL, best = NULL, newdata,
nwts, loss, eps = 1e-3)

##### Arguments

- tree
fitted model object of class

`tree`

. This is assumed to be the result of some function that produces an object with the same named components as that returned by the`tree()`

function.- k
cost-complexity parameter defining either a specific subtree of

`tree`

(`k`

a scalar) or the (optional) sequence of subtrees minimizing the cost-complexity measure (`k`

a vector). If missing,`k`

is determined algorithmically.- best
integer requesting the size (i.e. number of terminal nodes) of a specific subtree in the cost-complexity sequence to be returned. This is an alternative way to select a subtree than by supplying a scalar cost-complexity parameter

`k`

. If there is no tree in the sequence of the requested size, the next largest is returned.- newdata
data frame upon which the sequence of cost-complexity subtrees is evaluated. If missing, the data used to grow the tree are used.

- nwts
weights for the

`newdata`

cases.- method
character string denoting the measure of node heterogeneity used to guide cost-complexity pruning. For regression trees, only the default,

`deviance`

, is accepted. For classification trees, the default is`deviance`

and the alternative is`misclass`

(number of misclassifications or total loss).- loss
a matrix giving for each true class (row) the numeric loss of predicting the class (column). The classes should be in the order of the levels of the response. It is conventional for a loss matrix to have a zero diagonal. The default is 0--1 loss.

- eps
a lower bound for the probabilities, used to compute deviances if events of predicted probability zero occur in

`newdata`

.

##### Details

Determines a nested sequence of subtrees of the supplied tree by
recursively "snipping" off the least important splits, based upon
the cost-complexity measure. `prune.misclass`

is an abbreviation for
`prune.tree(method = "misclass")`

for use with `cv.tree`

.

If `k`

is supplied, the optimal subtree for that value is returned.

The response as well as the predictors referred to in the right side
of the formula in `tree`

must be present by name in
`newdata`

. These data are dropped down each tree in the
cost-complexity sequence and deviances or losses calculated by
comparing the supplied response to the prediction. The function
`cv.tree()`

routinely uses the `newdata`

argument
in cross-validating the pruning procedure. A `plot`

method
exists for objects of this class. It displays the value of the
deviance, the number of misclassifications or the total loss for
each subtree in the cost-complexity sequence. An additional axis
displays the values of the cost-complexity parameter at each subtree.

##### Value

If `k`

is supplied and is a scalar, a `tree`

object is
returned that minimizes the cost-complexity measure for that `k`

.
If `best`

is supplied, a `tree`

object of size `best`

is returned. Otherwise, an object of class `tree.sequence`

is returned. The object contains the following components:

number of terminal nodes in each tree in the cost-complexity pruning sequence.

total deviance of each tree in the cost-complexity pruning sequence.

the value of the cost-complexity pruning parameter of each tree in the sequence.

##### Examples

```
# NOT RUN {
data(fgl, package="MASS")
fgl.tr <- tree(type ~ ., fgl)
plot(print(fgl.tr))
fgl.cv <- cv.tree(fgl.tr,, prune.tree)
for(i in 2:5) fgl.cv$dev <- fgl.cv$dev +
cv.tree(fgl.tr,, prune.tree)$dev
fgl.cv$dev <- fgl.cv$dev/5
plot(fgl.cv)
# }
```

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