CORElearn-package {CORElearn}R Documentation

R port of CORElearn

Description

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.

Details

The main functions are

Some of the internal structures of the C++ part are described in CORElearn-internal.

Author(s)

Marko Robnik-Sikonja, Petr Savicky

References

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/

See Also

CoreModel, predict.CoreModel, modelEval, attrEval, ordEval, plot.ordEval, optionCore, paramCoreIO, infoCore, versionCore, CORElearn-internal, classDataGen, regDataGen, ordDataGen.

Examples

# 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)  


[Package CORElearn version 0.9.22 Index]