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. Ifobjectsis set toTRUE, the constructed objects are returned in the list column namedobject.
Examples
as.data.table(mlr_tuning_spaces)
#> key
#> 1: classif.glmnet.default
#> 2: classif.glmnet.rbv2
#> 3: classif.kknn.default
#> 4: classif.kknn.rbv2
#> 5: classif.ranger.default
#> 6: classif.ranger.rbv2
#> 7: classif.rpart.default
#> 8: classif.rpart.rbv2
#> 9: classif.svm.default
#> 10: classif.svm.rbv2
#> 11: classif.xgboost.default
#> 12: classif.xgboost.rbv2
#> 13: regr.glmnet.default
#> 14: regr.glmnet.rbv2
#> 15: regr.kknn.default
#> 16: regr.kknn.rbv2
#> 17: regr.ranger.default
#> 18: regr.ranger.rbv2
#> 19: regr.rpart.default
#> 20: regr.rpart.rbv2
#> 21: regr.svm.default
#> 22: regr.svm.rbv2
#> 23: regr.xgboost.default
#> 24: regr.xgboost.rbv2
#> key
#> label
#> 1: Default GLM with Elastic Net Regularization Classification
#> 2: RandomBot GLM with Elastic Net Regularization Classification
#> 3: Default k-Nearest-Neighbor Classification
#> 4: RandomBot k-Nearest-Neighbor Classification
#> 5: Default Ranger Classification
#> 6: RandomBot Ranger Classification
#> 7: Default Classification Tree
#> 8: RandomBot Classification Tree
#> 9: Default Support Vector Machine Classification
#> 10: RandomBot Support Vector Machine Classification
#> 11: Default Extreme Gradient Boosting Classification
#> 12: RandomBot Extreme Gradient Boosting Classification
#> 13: Default GLM with Elastic Net Regularization Regression
#> 14: RandomBot GLM with Elastic Net Regularization Regression
#> 15: Default k-Nearest-Neighbor Regression
#> 16: RandomBot k-Nearest-Neighbor Regression
#> 17: Default Ranger Regression
#> 18: RandomBot Ranger Regression
#> 19: Default Regression Tree
#> 20: RandomBot Regression Tree
#> 21: Default Support Vector Machine Regression
#> 22: RandomBot Support Vector Machine Regression
#> 23: Default Extreme Gradient Boosting Regression
#> 24: RandomBot Extreme Gradient Boosting Regression
#> 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>: Default Ranger Classification
#> 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>: Default Ranger Classification
#> 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