semiArtificial: Generator of Semi-Artificial Data

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.

Version: 2.4.1
Imports: CORElearn (≥ 1.50.3), RSNNS, MASS, nnet, cluster, fpc, stats, timeDate, robustbase, ks, logspline, methods, mcclust, flexclust, StatMatch
Published: 2021-09-23
DOI: 10.32614/CRAN.package.semiArtificial
Author: Marko Robnik-Sikonja
Maintainer: Marko Robnik-Sikonja <marko.robnik at>
License: GPL-3
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: semiArtificial results


Reference manual: semiArtificial.pdf


Package source: semiArtificial_2.4.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): semiArtificial_2.4.1.tgz, r-oldrel (arm64): semiArtificial_2.4.1.tgz, r-release (x86_64): semiArtificial_2.4.1.tgz, r-oldrel (x86_64): semiArtificial_2.4.1.tgz
Old sources: semiArtificial archive

Reverse dependencies:

Reverse imports: ExplainPrediction


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