Contains methods to generate and evaluate semi-artificial 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 |
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Author | Marko Robnik-Sikonja |
Maintainer | Marko Robnik-Sikonja <marko.robnik@fri.uni-lj.si> |
License | GPL-3 |
Version | 2.4.1 |
URL | http://lkm.fri.uni-lj.si/rmarko/software/ |
Package repository | View on CRAN |
Installation |
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