| decisionTree.SDA {symbolicDA} | R Documentation |
Decison tree for symbolic data
Description
Optimal split based decision tree for symbolic objects
Usage
decisionTree.SDA(sdt,formula,testSet,treshMin=0.0001,treshW=-1e10,
tNodes=NULL,minSize=2,epsilon=1e-4,useEM=FALSE,
multiNominalType="ordinal",rf=FALSE,rf.size,objectSelection)
Arguments
sdt |
Symbolic data table |
formula |
formula as in ln function |
testSet |
a vector of integers indicating classes to which each objects are allocated in learnig set |
treshMin |
parameter for tree creation algorithm |
treshW |
parameter for tree creation algorithm |
tNodes |
parameter for tree creation algorithm |
minSize |
parameter for tree creation algorithm |
epsilon |
parameter for tree creation algorithm |
useEM |
use Expectation Optimalization algorithm for estinating conditional probabilities |
multiNominalType |
"ordinal" - functione treats multi-nominal data as ordered or "nominal" functione treats multi-nomianal data as unordered (longer perfomance times) |
rf |
if TRUE symbolic variables for tree creation are randomly chosen like in random forest algorithm |
rf.size |
the number of variables chosen for tree creation if rf is true |
objectSelection |
optional, vector with symbolic object numbers for tree creation |
Details
For futher details see ../doc/decisionTree_SDA.pdf
Value
nodes |
nodes in tree |
nodeObjects |
contribution of each objects nodes in tree |
conditionalProbab |
conditional probability of belonginess of nodes te classes |
prediction |
predicted classes for objects from testSet |
Author(s)
Andrzej Dudek andrzej.dudek@ue.wroc.pl Marcin Pelka marcin.pelka@ue.wroc.pl
Department of Econometrics and Computer Science, University of Economics, Wroclaw, Poland http://keii.ue.wroc.pl/symbolicDA/
References
Billard L., Diday E. (eds.) (2006), Symbolic Data Analysis, Conceptual Statistics and Data Mining, John Wiley & Sons, Chichester.
Bock H.H., Diday E. (eds.) (2000), Analysis of symbolic data. Explanatory methods for extracting statistical information from complex data, Springer-Verlag, Berlin.
Diday E., Noirhomme-Fraiture M. (eds.) (2008), Symbolic Data Analysis with SODAS Software, John Wiley & Sons, Chichester.
See Also
bagging.SDA,boosting.SDA,random.forest.SDA,draw.decisionTree.SDA
Examples
# Example 1
# LONG RUNNING - UNCOMMENT TO RUN
# File samochody.xml needed in this example
# can be found in /inst/xml library of package
#sda<-parse.SO("samochody")
#tree<-decisionTree.SDA(sda, "Typ_samochodu~.", testSet=1:33)
#summary(tree) # a very gerneral information
#tree # summary information