Changelog 2.1
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