Description Usage Arguments Details Value Author(s) See Also Examples
Summary function for an imptree object, assesses accuracy achieved on training data and further tree properties.
1 2 3 4 5 6 |
object |
An object of class |
utility |
Utility for the utility based accuracy measure for a vacuous prediction result (default: 0.65). |
dominance |
Dominace criterion to be applied when predicting
classes. This may either be |
... |
Further arguments are ignored at the moment. |
x |
an object of class |
An existence check on the stored C++ object reference is carried
out at first. If the reference is not valid the original call
for "object"
is printed as error.
A named list of class summary.imptree
containing
the tree creation call, accuracy on the training data, meta data
and supplied the utility and dominance criterion for evaluation.
call |
Call to create the tree |
utility |
Supplied utility, or its default value |
dominance |
Supplied dominace criterion, or its default value |
sizes |
List containing the overall number and number of indeterminate predictions on training data |
acc |
named vector containing the accuracy measures
on training data with nicer names (without size information)
(see |
meta |
named vector containing the tree's depth, number of leaves and number of nodes |
The printing function returns the
summary.imptree
object invisibly.
Paul Fink Paul.Fink@stat.uni-muenchen.de
imptree
, predict.imptree
,
for information on a single node node_imptree
1 2 3 4 5 6 7 8 9 10 11 | data("carEvaluation")
## create a tree with IDM (s=1) to full size
## carEvaluation, leaving the first 10 observations out
ip <- imptree(acceptance~., data = carEvaluation[-(1:10),],
method="IDM", method.param = list(splitmetric = "globalmax", s = 1),
control = list(depth = NULL, minbucket = 1))
## summary including prediction on training data
summary(ip) # default prediction
summary(ip, dominance = "max") # different prediction parameter
|
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