Contains methods to generate and evaluate semiartificial data sets. Based on a given data set different methods learn data properties using machine learning algorithms and generate new data with the same properties. The package currently includes the following data generators: i) a RBF network based generator using rbfDDA() from package 'RSNNS', ii) a Random Forest based generator for both classification and regression problems iii) a density forest based generator for unsupervised data Data evaluation support tools include: a) single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance b) evaluation based on clustering using Adjusted Rand Index (ARI) and FM c) evaluation based on classification performance with various learning models, e.g., random forests.
Package details 


Author  Marko RobnikSikonja 
Date of publication  20170331 06:13:09 UTC 
Maintainer  Marko RobnikSikonja <marko.robnik@fri.unilj.si> 
License  GPL3 
Version  2.2.5 
URL  http://lkm.fri.unilj.si/rmarko/software/ 
Package repository  View on CRAN 
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