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
Author:	Marko Robnik-Sikonja
Maintainer:	Marko Robnik-Sikonja <marko.robnik at fri.uni-lj.si>
License:	GPL-3
URL:	http://lkm.fri.uni-lj.si/rmarko/software/
NeedsCompilation:	no
Materials:	ChangeLog
CRAN checks:	semiArtificial results

Documentation:

Reference manual:

semiArtificial.pdf

Downloads:

Package source:	semiArtificial_2.4.1.tar.gz
Windows binaries:	r-devel: semiArtificial_2.4.1.zip, r-release: semiArtificial_2.4.1.zip, r-oldrel: semiArtificial_2.4.1.zip
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

Linking:

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