summary.imptree {imptree} | R Documentation |
Summary function for an imptree object, assesses accuracy achieved on training data and further tree properties.
## S3 method for class 'imptree' summary(object, utility = 0.65, dominance = c("strong", "max"), ...) ## S3 method for class 'summary.imptree' print(x, ...)
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
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