| random.forest.SDA {symbolicDA} | R Documentation |
Random forest algorithm for optimal split based decision tree for symbolic objects
Description
Random forest algorithm for optimal split based decision tree for symbolic objects
Usage
random.forest.SDA(sdt,formula,testSet, mfinal = 100,...)
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 |
mfinal |
number of partial models generated |
... |
arguments passed to decisionTree.SDA function |
Details
random.forest.SDA implements Breiman's random forest algorithm for classification of symbolic data set.
Value
Section details goes here
Author(s)
Andrzej Dudek andrzej.dudek@ue.wroc.pl Marcin Pełka 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,decisionTree.SDA
Examples
# Example will be available in next version of package, thank You for your patience :-)