Version 2.4

• WrappedModel printer was slightly improved
• ReampleResult now stores the runtime it took to resample in a slot
• getTaskFormula / getTaskFormulaAsString have new argument 'explicit.features'
• getTaskData now has recodeY = "drop.levels" which drops empty factor levels
• option fix.factors in makeLearner was renamed to fix.factors.prediction for
  clarity
• showHyperPars was removed. getParamSet does exactly the same thing
• 'resample' and 'benchmark' got the argument keep.pred,
  setting it to FALSE allows to discard the prediction objects to save memory
• we had to slightly change how the mem usage is reported in tuning and feature
  selection
  See TuneControl and FeatSelControl where it is documented what is done now.
• tuneIrace: allows to set the precision / digits within irace (using the argument
  'digits' in makeTuneControlIrace); default is maximum precision
• for plotting in general we try to introduce a "data layer", so the data can be
  generated independently of the plotting first, into well-defined objects;
  these can then be plotted with mlr or custom code;
  the naming scheme is always generateData and plot
• getFilterValues is deprecated in favor of generateFilterValuesData
• plotFilterValues can now plot multiple filter methods using facetting
• plotROCRCurves has been rewritten to use ggplot2
• classif.ada: added "loss" hyperpar
• add missings properties to all ctree and cforest methods:
  regr/classif for ctree, regr/classif/surv for cforest, and regr/classif for blackboost
• learner xgboost was removed, because the package is not on CRAN anymore,
  unfortunately
• reg.km: added param 'iso'
• classif.mda: added param 'start.method' and changed its default to 'lvq', added
  params 'sub.df', 'tot.df' and 'criterion'
• classif.randomForest: 'sampsize' can now be an int vector (instead of a scalar)
• plotThreshVsPerf and plotLearningCurve now have param 'facet'

new functions

• getTaskSize
• getNestedTuneResultsX, getNestedTuneResultsOptPathDf
• tuneDesign
• generateROCRCurvesData, generateFilterValuesData, generateLearningCurveData,
  plotLearningCurve, generateThreshVsPerfData, plotThreshVsPerf,
• generateThreshVsPerfData accepts Prediction, ResampleResult, lists of
  ResampleResult, and BenchmarkResult objects.
• experimental ggvis functions: plotROCRCurvesGGVIS, plotLearningCurveGGVIS,
  plotTuneMultiCritResultGGVIS, plotThreshVsPerfGGVIS, and plotFilterValuesGGVIS

new learners:

• classif.bst
• classif.hdrda
• classif.nodeHarvest
• classif.pamr
• classif.rFerns
• classif.sparseLDA
• regr.bst
• regr.frbs
• regr.nodeHarvest
• regr.slim

new measures:

• brier