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.
|Date of publication||2017-03-31 06:13:09 UTC|
|Maintainer||Marko Robnik-Sikonja <email@example.com>|
cleanData: Rejection of new instances based on their distance to...
dataSimilarity: Evaluate statistical similarity of two data sets
dsClustCompare: Evaluate clustering similarity of two data sets
newdata: Generate semi-artificial data using a generator
performanceCompare: Evaluate similarity of two data sets based on predictive...
rbfDataGen: A data generator based on RBF network
semiArtificial-package: Generation and evaluation of semi-artificial data
treeEnsemble: A data generator based on forest
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