mlr 2.1: 2014-07-21

  • mlr now supports multi-criteria tuning
  • mlr now supports cluster analysis (experimental)
  • improve makeWeightedClassesWrapper: Hyperparams for class weighting are now supported, too.
  • removed fix.factors option from randomForest, but added it in general to makeLearner, so it now works for all learners. Helps when feature factor levels where dropped in newdata prediction data.frames
  • more consistent results for tuning algorithms and parameters with “trafos” : we always return the optimal settings on the transformed scale, but in the opt.path in the original scale.
  • fix a bug when feature filtering resulted in a NoFeatureModel
  • resample now returns a data.frame “err.mgs” or error messages that might have occurred during resampling
  • stratified resampling for survival

new learners:

  • classif.cforest
  • classif.glmnet
  • classif.plsdaCaret
  • regr.cforest
  • regr.glmnet
  • regr.svm
  • surv.cforest
  • cluster.SimpleKMeans
  • cluster.EM
  • cluster.XMeans

new measures

  • bac
  • db, dunn, g1, g2, silhouette

new functions

  • makeClusterTask
  • removeHyperPars
  • tuneParamsMultiCrit
  • makeTuneMultiCritControlGrid, makeTuneMultiCritControlRandom, makeTuneMultiCritControlNSGA2
  • plotTuneMultiCritResult
  • getFailureModelMsg