Version 2.9
mlr 2.9:
CRAN release: 2016-08-03
functions - general
- various cleanups that removed unused code
- subsetTask, getTaskData: arg “features” now also accepts logical and integer
- removeConstantFeatures now also operates on data.frames and makeRemoveConstantFeaturesWrapper can be used to augment a learner with this preprocessing step.
- normalizeFeatures, createDummyFeatures: arg ‘exclude’ was replaced by ‘cols’
- normalizeFeatures is now S3 and can be called also on data.frames
- SMOTEWrapper: fix a bug where “sw.nn” was not correctly passed down
- fixed a bug that caused hyperparameters to be not passed on correctly in the ModelMultiplexer in some cases
- fix bug with NoFeaturesModel and ModelMultiplexer
- fix small bug in DownsampleWrapper when trained with weights
- getNestedTuneResultsOptPathDf: added new arg “trafo”
- improve documentation for permutation.importance filter and perform slight argument renaming to fix potential name clashes
- plotPartialDependence can plot classification tasks with more than one interacted features now
- generateFilterValuesData: added argument ‘more.args’
- add pretty.names arguments to plots that show learner short names instead of IDs
- addition of ‘data’ argument to plotPartialDependence which adds the training data to the graph
- added new arguments “facet.wrap.nrow” and “facet.wrap.ncol” which enable arrangement of facets in rows and columns to plotting functions
functions - new
- generateHyperParsEffectData, plotHyperParsEffect
- makeMultilabelClassifierChainsWrapper, makeMultilabelDBRWrapper makeMultilabelNestedStackingWrapper, makeMultilabelStackingWrapper
- makeConstantClassWrapper
- generateFunctionalANOVAData
functions - renamed
- generatePartialPrediction to generatePartialDependence
- plotPartialPrediction to plotPartialDependence
- plotPartialPredictionGGVIS to plotPartialDependenceGGVIS
learners - general
- fixed weight handling and weight tag for some learners
- remove unnecessary linear.output parameter for classif.neuralnet
- remove unsupported KSVM parameter value stringdot
- fix some bartMachine compatibility issues
- classif.ranger, regr.ranger and surv.ranger: now respect unordered factors by default
- clean up randomForestSRC and randomForestSRCSyn learners
- the “penalized” learner were restructured and improved (params were added), also see below.
- add stability.nugget parameter for “regr.km”
- classif.blackboost, regr.blackboost: made sure that arg “stump” is passed on correctly
- fixed parameter values for WEKA learners IBk, J48, PART, EM, SimpleKMeans, XMeans
- classif.glmboost, regr.glmboost: add parameters stopintern and trace
learners - new
- classif.C50
- classif.gausspr
- classif.penalized.fusedlasso
- classif.penalized.lasso
- classif.penalized.ridge
- classif.h2o.deeplearning
- classif.h2o.gbm
- classif.h2o.glm
- classif.h2o.randomForest
- classif.rrf
- regr.penalized.fusedlasso
- regr.gausspr
- regr.glm
- regr.GPfit
- regr.h2o.deeplearning
- regr.h2o.gbm
- regr.h2o.glm
- regr.h2o.randomForest
- regr.rrf
- surv.cv.CoxBoost
- surv.penalized.fusedlasso
- surv.penalized.lasso
- surv.penalized.ridge
- cluster.kkmeans
- multilabel.randomforestSRC
measures - general
- updated gmean measure and unit test, added reference to formula of gmean
- makeCostMeasure: removed arg “task”, names of cost matrix are checked on measure calculation