Changelog 2.8
mlr 2.8: 2016-02-13
- Feature filter “univariate” had a bad name, was deprecated and is now called “univariate.model.score”. The new one also has better defaults.
- (generate/plot)PartialPrediction: added new arg “geom” for tile plots
- small fix for plotBMRSummary
- the ModelMultiplexer inherits its predict.type from the base learners now
- check that learners in an ensemble have the same predict.type
- new function getBMRModels to extract stored models from a benchmark result
- Fixed a bug where several learners from the LiblineaR package (“classif.LiblineaRL2LogReg”, “classif.LiblineaRL2SVC”, “regr.LiblineaRL2L2SVR”) were calling the wrong value for “type” (0) and thus training the wrong model.
- Fixed a bug where the resampling objects hout, cv2, cv3, cv5, cv10 were not documented in the ResampleDesc help page
- regr.xgboost, classif.xgboost: add feval param
- fixed a bug in irace tuning interface with unamed discrete values
- Fixed bugs in “jackknife” and “bootstrap” se estimators for regr.randomForest.
- Added “sd” estimator for regr.randomForest.
- Fixed a mini bug in ModelMultiplexer where hyperpars that are only needed in predict were not passed down correctly
- Fixed a bug where the function capLargeValues wasn’t working if you passed a task.
- capLargeValues now has a new argument “target”, to prevent from capping response values.
- classif.gbm, regr.gbm: Updated possible ‘distribution’ settings a bit.
- oversample, undersample, makeOversampleWrapper, makeUndersampleWrapper, makeOverBaggingWrapper: Added arguments to specifically select the sampled class.