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 adata.table::data.table()
with fields "key", "label", "learner", and "n_values" as columns. Ifobjects
is set toTRUE
, the constructed objects are returned in the list column namedobject
.
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