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.
1.50.3), RSNNS, MASS, nnet, cluster, fpc, stats, timeDate, robustbase, ks, logspline, methods, mcclust, flexclust, StatMatch
||Marko Robnik-Sikonja <marko.robnik at fri.uni-lj.si>
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