Version 2.11
mlr 2.11:
CRAN release: 2017-03-15
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
functions - general
- 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.
functions - new
- 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
learners - general
- 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