Cost-complexity Pruning of Tree Object

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

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)


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.


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.


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.


data frame upon which the sequence of cost-complexity subtrees is evaluated. If missing, the data used to grow the tree are used.


weights for the newdata cases.


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).


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.


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


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.


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.

  • prune.tree
  • prune.misclass
data(fgl, package="MASS") <- tree(type ~ ., fgl)
plot(print( <- cv.tree(,, prune.tree)
for(i in 2:5)$dev <-$dev +
   cv.tree(,, prune.tree)$dev$dev <-$dev/5
# }
Documentation reproduced from package tree, version 1.0-40, License: GPL-2 | GPL-3

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