Dictionary of Tuning Spaces

A simple mlr3misc::Dictionary storing objects of class TuningSpace. Each tuning space has an associated help page, see mlr_tuning_spaces_[id].

Format

R6::R6Class object inheriting from mlr3misc::Dictionary.

Methods

See mlr3misc::Dictionary.

S3 methods

• as.data.table(dict, ..., objects = FALSE)
  mlr3misc::Dictionary -> data.table::data.table()
  Returns a data.table::data.table() with fields "key", "label", "learner", and "n_values" as columns. If objects is set to TRUE, the constructed objects are returned in the list column named object.

Examples

as.data.table(mlr_tuning_spaces)
#>                         key                                 label
#>  1:  classif.glmnet.default       Classification GLM with Default
#>  2:     classif.glmnet.rbv2     Classification GLM with RandomBot
#>  3:    classif.kknn.default      Classification KKNN with Default
#>  4:       classif.kknn.rbv2    Classification KKNN with RandomBot
#>  5:  classif.ranger.default    Classification Ranger with Default
#>  6:     classif.ranger.rbv2  Classification Ranger with RandomBot
#>  7:   classif.rpart.default     Classification Rpart with Default
#>  8:      classif.rpart.rbv2   Classification Rpart with RandomBot
#>  9:     classif.svm.default       Classification SVM with Default
#> 10:        classif.svm.rbv2     Classification SVM with RandomBot
#> 11: classif.xgboost.default   Classification XGBoost with Default
#> 12:    classif.xgboost.rbv2 Classification XGBoost with RandomBot
#> 13:     regr.glmnet.default           Regression GLM with Default
#> 14:        regr.glmnet.rbv2         Regression GLM with RandomBot
#> 15:       regr.kknn.default          Regression KKNN with Default
#> 16:          regr.kknn.rbv2        Regression KKNN with RandomBot
#> 17:     regr.ranger.default        Regression Ranger with Default
#> 18:        regr.ranger.rbv2      Regression Ranger with RandomBot
#> 19:      regr.rpart.default         Regression Rpart with Default
#> 20:         regr.rpart.rbv2       Regression Rpart with RandomBot
#> 21:        regr.svm.default           Regression SVM with Default
#> 22:           regr.svm.rbv2         Regression SVM with RandomBot
#> 23:    regr.xgboost.default       Regression XGBoost with Default
#> 24:       regr.xgboost.rbv2     Regression XGBoost with RandomBot
#>                         key                                 label
#>             learner n_values
#>  1:  classif.glmnet        2
#>  2:  classif.glmnet        2
#>  3:    classif.kknn        3
#>  4:    classif.kknn        1
#>  5:  classif.ranger        4
#>  6:  classif.ranger        8
#>  7:   classif.rpart        3
#>  8:   classif.rpart        4
#>  9:     classif.svm        4
#> 10:     classif.svm        5
#> 11: classif.xgboost        8
#> 12: classif.xgboost       13
#> 13:     regr.glmnet        2
#> 14:     regr.glmnet        2
#> 15:       regr.kknn        3
#> 16:       regr.kknn        1
#> 17:     regr.ranger        4
#> 18:     regr.ranger        7
#> 19:      regr.rpart        3
#> 20:      regr.rpart        4
#> 21:        regr.svm        4
#> 22:        regr.svm        5
#> 23:    regr.xgboost        8
#> 24:    regr.xgboost       13
#>             learner n_values
mlr_tuning_spaces$get("classif.ranger.default")
#> <TuningSpace:classif.ranger.default>: Classification Ranger with Default
#>                 id lower upper      levels logscale
#> 1:      mtry.ratio   0.0     1                FALSE
#> 2:         replace    NA    NA  TRUE,FALSE    FALSE
#> 3: sample.fraction   0.1     1                FALSE
#> 4:       num.trees   1.0  2000                FALSE
lts("classif.ranger.default")
#> <TuningSpace:classif.ranger.default>: Classification Ranger with Default
#>                 id lower upper      levels logscale
#> 1:      mtry.ratio   0.0     1                FALSE
#> 2:         replace    NA    NA  TRUE,FALSE    FALSE
#> 3: sample.fraction   0.1     1                FALSE
#> 4:       num.trees   1.0  2000                FALSE