mlr 2.15.0 2019-08-06

Breaking

  • Instead of a wide data.frame filter values are now returned in a long (tidy) tibble. This makes it easier to apply post-processing methods (like group_by(), etc) (@pat-s, #2456)
  • benchmark() does not store the tuning results ($extract slot) anymore by default. If you want to keep this slot (e.g. for post tuning analysis), set keep.extract = TRUE. This change originated from the fact that the size of BenchmarkResult objects with extensive tuning got very large (~ GB) which can cause memory problems during runtime if multiple benchmark() calls are executed on HPCs.
  • benchmark() does not store the created models ($models slot) anymore by default. The reason is the same as for the $extract slot above. Storing can be enabled using models = TRUE.

functions - general

learners - general

  • classif.liquidSVM and regr.liquidSVM have been removed because liquidSVM has been removed from CRAN.
  • fixed a bug that caused an incorrect aggregation of probabilities in some cases. The bug existed since quite some time and was exposed due to the change of data.tables default in rbindlist(). See #2578 for more information. (@mllg, #2579)
  • regr.randomForest gains three new methods to estimate the standard error:
    • se.method = "jackknife"
    • se.method = "bootstrap"
    • se.method = "sd"
      See ?regr.randomForest for more details.
      regr.ranger relies on the functions provided by the package (“jackknife” and “infjackknife” (default))
      (@jakob-r, #1784)
  • regr.gbm now supports quantile distribution (@bthieurmel, #2603)
  • classif.plsdaCaret now supports multiclass classification (@GegznaV, #2621)

functions - general

learners - new

  • add learner cluster.MiniBatchKmeans from package ClusterR (@Prasiddhi, #2554)

function - general

filters - general

filters - new

  • Ensemble features are now supported. These filters combine multiple single filters to create a final ranking based on certain statistical operations. All new filters are listed in a dedicated section “ensemble filters” in the tutorial. Tuning of simple features is not supported yet because of a missing feature in ParamHelpers. (@pat-s, #2456)