Changelog 2.4
mlr 2.4: 2015-06-13
- 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 generate
Data 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