# decisionTree.SDA: Decison tree for symbolic data In symbolicDA: Analysis of Symbolic Data

 decisionTree.SDA 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.

`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 Feb. 16, 2023, 6 p.m.