Changelog 2.0

mlr 2.0: 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 measures

  • multiauc
  • cindex
  • meancosts, mcp

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