CORElearn-package {CORElearn} | R Documentation |
The package CORElearn is an R port of CORElearn data mining system. It provides various classification and regression models as well as algorithms for feature selection and evaluation.
The main functions are
CoreModel
which constructs classification or regression model.
predict.CoreModel
predicts with classification model labels and probabilities of new instances.
For regression models it returns the predicted function value.
modelEval
computes some statistics from predictions
attrEval
evaluates the quality of the attributes (dependent variables)
with the selected heuristic method.
Feature evaluation algorithms are various variants of Relief algorithms (ReliefF, RReliefF,
cost-sensitive ReliefF, ..), gain ratio, gini-index, MDL, DKM, information gain, MSE, MAE, ....
ordEval
evaluates ordinal attributes with ordEval algorithm and visualizes them with plot.ordEval
,
infoCore
outputs certain information about CORElearn methods,
optionCore
prints short description of a given parameter,
paramCoreIO
reads/writes parameters for given model from/to file,
versionCore
outputs version of the package from underlying C++ library.
Some of the internal structures of the C++ part are described in CORElearn-internal
.
Marko Robnik-Sikonja, Petr Savicky
Marko Robnik-Sikonja, Igor Kononenko: Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning Journal, 53:23-69, 2003
Marko Robnik-Sikonja: Improving Random Forests. In J.-F. Boulicaut et al.(Eds): ECML 2004, LNAI 3210, Springer, Berlin, 2004, pp. 359-370
Marko Robnik-Sikonja, Koen Vanhoof: Evaluation of ordinal attributes at value level. Knowledge Discovery and Data Mining, 14:225-243, 2007
Marko Robnik-Sikonja: Experiments with Cost-sensitive Feature Evaluation. In Lavrac et al.(eds): Machine Learning, Proceedings of ECML 2003, Springer, Berlin, 2003, pp. 325-336
Majority of these references are available also from http://lkm.fri.uni-lj.si/rmarko/papers/
CoreModel
,
predict.CoreModel
,
modelEval
,
attrEval
,
ordEval
,
plot.ordEval
,
optionCore
,
paramCoreIO
,
infoCore
,
versionCore
,
CORElearn-internal
,
classDataGen
,
regDataGen
,
ordDataGen
.
# load the package library(CORElearn) cat(versionCore(),"\n") # use iris data set # build random forests model with certain parameters model <- CoreModel(Species ~ ., iris, model="rf", selectionEstimator="MDL",minNodeWeight=5,rfNoTrees=100) print(model) # prediction with node distribution pred <- predict.CoreModel(model, iris, rfPredictClass=FALSE) print(pred) # Model evaluation mEval <- modelEval(model, iris[["Species"]], pred$class, pred$prob) print(mEval) # evaluate features in given data set with selected method estReliefF <- attrEval(Species ~ ., iris, estimator="ReliefFexpRank", ReliefIterations=30) print(estReliefF) # evaluate ordered features with ordEval profiles <- ordDataGen(200) est <- ordEval(class ~ ., profiles, ordEvalNoRandomNormalizers=100) print(est)