semiArtificial: Generator of Semi-Artificial Data

Package semiArtificial 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 generator: -a RBF network based generator using rbfDDA from RSNNS package, -a Random Forest based generator for both classification and regression problems -a density forest based generator for unsupervised data Data evaluation support tools include: -single attribute based statistical evaluation: mean, median, standard deviation, skewness, kurtosis, medcouple, L/RMC, KS test, Hellinger distance -evaluation based on clustering using Adjusted Rand Index (ARI) and FM -evaluation based on classification performance with various learning models, eg, random forests.

Downloads:

Reverse dependencies: