Version 2.11 • mlr

general

The internal class naming of the task descriptions have been changed causing probable incompatibilities with tasks generated under old versions.
New option on.error.dump to include dumps that can be inspected with the debugger with errors
mlr now supports tuning with Bayesian optimization with mlrMBO

tuneParams: fixed a small and obscure bug in logging for extremely large ParamSets
getBMR-operators: now support “drop” argument that simplifies the resulting list
configureMlr: added option “on.measure.not.applicable” to handle situations where performance cannot be calculated and one wants NA instead of an error - useful in, e.g., larger benchmarks
tuneParams, selectFeatures: removed memory stats from default output for performance reasons (can be restored by using a control object with “log.fun” = “memory”)
listLearners: change check.packages default to FALSE
tuneParams and tuneParamsMultiCrit: new parameter resample.fun to specify a custom resampling function to use.
Deprecated: getTaskDescription, getBMRTaskDescriptions, getRRTaskDescription. New names: getTaskDesc, getBMRTaskDescs, getRRTaskDesc.

getOOBPreds: get out-of-bag predictions from trained models for learners that store them – these learners have the new “oobpreds” property
listTaskTypes, listLearnerProperties
getMeasureProperties, hasMeasureProperties, listMeasureProperties
makeDummyFeaturesWrapper: fuse a learner with a dummy feature creator
simplifyMeasureNames: shorten measure names to the actual measure, e.g. mmce.test.mean -> mmce
getFailureModelDump, getPredictionDump, getRRDump: get error dumps
batchmark: Function to run benchmarks with the batchtools package on high performance computing clusters
makeTuneControlMBO: allows Bayesian optimization

classif.plsdaCaret: added parameter “method”.
regr.randomForest: refactored se-estimation code, improved docs and default is now se.method = “jackknife”.
regr.xgboost, classif.xgboost: removed “factors” property as these learners do not handle categorical features – factors are silently converted to integers internally, which may misinterpret the structure of the data
glmnet: control parameters are reset to factory settings before applying custom settings and training and set back to factory afterwards