Skip to content

Version 2.3
Compare
Choose a tag to compare
@pat-s pat-s released this 23 Jun 15:58
· 2862 commits to master since this release
v2.3
b702005
  • resample now returns an object of class ResampleResult (downward compatible)
    to allow for a print method.
  • resampling on features now supported for an arbitrary number of factor features
  • mlr supports ViperCharts plots now
  • ROC plot via ROCR can now be created automatically, before you had to call
    asROCRPrediction,
    then construct the plots via ROCR your self. See plotROCRCurves
  • all mlr measures now have slots "name" and "note"
  • exported a few very simple "getters" for tasks, see below
  • in makeLearner a probability predict.threshold can be set for classifiers, also
    see setPredictThreshold
  • in the control objects for tuning and feature selection, the user can now enable
    threshold tuning
  • in the control objects for tuning and feature selection, the user can now define
    his own logging function
  • default console logging for tuneParams and selectFeatures is more informative,
    it displays time and memory info
  • updated some properties of some learners
  • Default arguments of classif.bartMachine, classif.randomForestSRC,
    regr.randomForestSRC and sur.randomForestSRC
    have been changed to allow missing data support with default settings.
  • externalized measure functions to be used on vectors.
  • some minor bug fixes
  • required basic learner packages are not loaded into the global namespace
    anymore, requireNamespace
    is used internally instead. this ensures less name clashes and name shadowing
  • resample passes dot arguments to the learner hyperpars
  • new option "on.par.out.of.bounds" to disable out-of-bound checks for model
    parameters
  • measures were slightly internally changed. they expose more properties (check
    ?Measure) and some now unnecessary object slots were removed
  • classif.lda and classif.qda now have hyperpar "predict.method"
  • filterFeatures and makeFilterWrapper gain an argument for mandatory features
  • plotLearnerPrediction has new option "err.size"
  • classif.plsDA and cluster.DBscan for now removed because of problems with the
    underlying learning algorithm
  • new aggregation test.join
  • the following models now can handle factors and ordereds by extra dummy or int
    encoding:
    classif.glmnet, regr.glmnet, surv.glmnet, surv.cvglmnet, surv.penalized,
    surv.optimCoxBoostPenalty, surv.glmboost, surv.CoxBoost

new functions

  • getTaskType, getTaskId, getTaskTargetNames
  • plotROCRCurves
  • plotViperCharts
  • measureSSE, measureMSE, measureRMSE, measureMEDSE, ...
  • PreprocWrapperCaret
  • setPredictThreshold

new learners:

  • classif.bdk
  • classif.binomial
  • classif.extraTrees
  • classif.probit
  • classif.xgboost
  • classif.xyf
  • regr.bartMachine
  • regr.bcart
  • regr.bdk
  • regr.bgp
  • regr.bgpllm
  • regr.blm
  • regr.brnn
  • regr.btgp
  • regr.btgpllm
  • regr.btlm
  • regr.cubist
  • regr.elmNN
  • regr.extraTrees
  • regr.laGP
  • regr.xgboost
  • regr.xyf
  • surv.rpart
Assets 2
Loading