decisionTree.SDA: Decison tree for symbolic data

View source: R/decisionTree.SDA.r

decisionTree.SDAR 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

symbolicDA documentation built on May 28, 2022, 1:08 a.m.