Version 2.0
mlr 2.0:
CRAN release: 2014-07-04
- mlr now supports survival analysis models (experimental)
- mlr now supports cost-sensitive learning with example-specific costs experimental)
- Some example tasks and data sets were added for simple access
- added FeatSelWrapper and getFeatSelResult
- performance functions now allows to compute multiple measures
- added multiclass.roc performance measure
- observation weights can now also be specified in the task
- added option on.learner.warning to configureMlr to suppress warnings in learners
- fixed a bug in stratified CV where elements where not distributed as evenly as possible when the split number did not divide the number of observation
- added class.weights param for classif.svm
- add fix.factors.prediction option to randomForest
- generic standard error estimation in randomForest and BaggingWrapper
- added fixup.data option to task constructors, so basic data cleanup can be performed
- show.info is now an option in configureMlr
- learners now support taggable properties that can be queried and changed. also see below.
- listLearners(forTask) was unified
- removed tuning via R’ optim method (makeTuneControlOptim), as the optimizers in there really make no sense for tuning
- Grid search was improved so one does not have to discretize parameters manually anymore (although this is still possible). Instead one now passes a ‘resolution’ argument. Internally we now use ParamHelpers::generateGridDesign for this.
- toy tasks were added for convenient usage: iris.task, sonar.task, bh.task they also also have corresponding resampling instances, so you directly start working, e.g., iris.rin
new learners:
- classif.knn
- classif.IBk
- classif.LiblineaRBinary
- classif.LiblineaRLogReg
- classif.LiblineaRMultiClass
- classif.linDA
- classif.plr
- classif.plsDA
- classif.rrlda
- regr.crs
- regr.IBk
- regr.mob
- surv.CoxBoost
- surv.coxph
- surv.glmboost
- surv.glmnet
- surv.penalized
- surv.randomForestSRC
new functions
- removeConstantFeatures, normalizeFeatures, dropFeatures, createDummyFeatures
- getTaskNFeats
- hasProperties, getProperties, setProperties, addProperties, removeProperties
- showHyperPars
- setId
- listMeasures
- isFailureModel
- plotLearnerPrediction
- plotThreshVsPerf
- holdout, subsample, crossval, repcv, bootstrapOOB, bootstrapB632, bootstrapB632plus
- listFilterMethods, getFilterValues, filterFeatures, makeFilterWrapper, plotFilterValues
- benchmark
- getPerformances, getAggrPerformances, getPredictions, getFilterResult, getTuneResult, getFeatSelResult
- oversample, undersample, makeOversampleWrapper, makeUndersampleWrapper
- smote, makeSmoteWrapper
- downsample, makeDownsampleWrapper
- makeWeightedClassesWrapper
- makeTuneControlGenSA
- makeModelMultiplexer, makeModelMultiplexerParamSet
- makeCostSensTask, makeCostSensClassifWrapper, makeCostSensRegrWrapper, makeCostsSensWeightedPairsLearner
- makeSurvTask
- impute, reimpute, makeImputeWrapper, lots of impute
, makeImputeMethod