mlr 2.10 is now on CRAN. Please update your package if you haven’t done so in a while.

Here is an overview of the changes:

functions – general

• fixed bug in resample when using predict = “train” (issue #1284)
• update to irace 2.0 – there are algorithmic changes in irace that may affect performance
• generateFilterValuesData: fixed a bug wrt feature ordering
• imputeLearner: fixed a bug when data actually contained no NAs
• print.Learner: if a learner hyperpar was set to value “NA” this was not displayed in printer
• makeLearner, setHyperPars: if you mistype a learner or hyperpar name, mlr uses fuzzy matching to suggest the 3 closest names in the message
• tuneParams: tuning with irace is now also parallelized, i.e., different learner configs are evaluated in parallel.
• benchmark: mini fix, arg ‘learners’ now also accepts class strings
• object printers: some mlr printers show head previews of data.frames. these now also print info on the total nr of rows and cols and are less confusing
• aggregations: have better properties now, they know whether they require training or test set evals
• the filter methods have better R docs
• filter randomForestSRC.var.select: new arg “method”
• filter mrmr: fixed some smaller bugs and updated properties
• generateLearningCurveData: also accepts single learner, does not require a list
• plotThreshVsPerf: added “measures” arg
• plotPartialDependence: can create tile plots with joint partial dependence on two features for multiclass classification by facetting across the classes
• generatePartialDependenceData and generateFunctionalANOVAData: expanded “fun” argument to allow for calculation of weights
• new “?mlrFamilies” manual page which lists all families and the functions belonging to it
• we are converging on data.table as a standard internally, this should not change any API behavior on the outside, though
• generateHyperParsEffectData and plotHyperParsEffect now support more than 2 hyperparameters
• linear.correlation, rank.correlation, anova.test: use Rfast instead of FSelector/custom implementation now, performance should be much better
• use of our own colAUC function instead of the ROCR package for AUC calculation to improve performance
• we output resample performance messages for every iteration now
• performance improvements for the auc measure
• createDummyFeatures supports vectors now
• removed the pretty.names argument from plotHyperParsEffect – labels can be set though normal ggplot2 functions on the returned object
• Fixed a bad bug in resample, the slot “runtime” or a ResampleResult, when the runtime was measured not in seconds but e.g. mins. R measures then potentially in mins, but mlr claimed it would be seconds.
• New “dummy” learners (that disregard features completely) can be fitted now for baseline comparisons, see “featureless” learners below.

functions – new

• filter: randomForest.importance
• generateFeatureImportanceData: permutation-based feature importance and local importance
• getFeatureImportanceLearner: new Learner API function
• getFeatureImportance: top level function to extract feature importance information
• calculateROCMeasures
• calculateConfusionMatrix: new confusion-matrix like function that calculates and tables many receiver operator measures
• makeLearners: create multiple learners at once
• getLearnerId, getLearnerType, getLearnerPredictType, getLearnerPackages
• getLearnerParamSet, getLearnerParVals
• getRRPredictionList
• addRRMeasure
• plotResiduals
• getLearnerShortName
• mergeBenchmarkResults

functions – renamed

• Renamed rf.importance filter (now deprecated) to randomForestSRC.var.rfsrc
• Renamed rf.min.depth filter (now deprecated) to randomForestSRC.var.select
• Renamed getConfMatrix (now deprecated) to calculateConfusionMatrix
• Renamed setId (now deprecated) to setLearnerId

functions – removed

• mergeBenchmarkResultLearner, mergeBenchmarkResultTask

learners – general

• classif.ada: fixed some param problem with rpart.control params
• classif.cforest, regr.cforest, surv.cforest: removed parameters “minprob”, “pvalue”, “randomsplits” as these are set internally and cannot be changed by the user
• regr.GPfit: some more params for correlation kernel
• classif.xgboost, regr.xgboost: can now properly handle NAs (property was missing and other problems), added “colsample_bylevel” parameter
• adapted {classif,regr,surv}.ranger parameters for new ranger version

learners – new

• multilabel.cforest
• surv.gbm
• regr.cvglmnet
• {classif,regr,surv}.gamboost
• classif.earth
• {classif,regr}.evtree
• {classif,regr}.evtree

learners – removed

• classif.randomForestSRCSyn, regr.randomForestSRCSyn: due to continued stability issues

measures – new

• ssr, qsr, lsr
• rrse, rae, mape
• kappa, wkappa
• msle, rmsle