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: <key>
#> key label
#> <char> <char>
#> 1: classif.ft_transformer.rtdl Classification FT-Transformer with RTDL
#> 2: classif.glmnet.default Classification GLM with Default
#> 3: classif.glmnet.rbv1 Classification GLM with RandomBot
#> 4: classif.glmnet.rbv2 Classification GLM with RandomBot
#> 5: classif.mlp.rtdl Classification MLP with RTDL
#> 6: classif.ranger.default Classification Ranger with Default
#> 7: classif.ranger.rbv1 Classification Ranger with RandomBot
#> 8: classif.ranger.rbv2 Classification Ranger with RandomBot
#> 9: classif.rpart.default Classification Rpart with Default
#> 10: classif.rpart.rbv1 Classification Rpart with RandomBot
#> 11: classif.rpart.rbv2 Classification Rpart with RandomBot
#> 12: classif.svm.default Classification SVM with Default
#> 13: classif.svm.rbv1 Classification SVM with RandomBot
#> 14: classif.svm.rbv2 Classification SVM with RandomBot
#> 15: classif.tab_resnet.rtdl Classification Tabular ResNet with RTDL
#> 16: classif.xgboost.default Classification XGBoost with Default
#> 17: classif.xgboost.rbv1 Classification XGBoost with RandomBot
#> 18: classif.xgboost.rbv2 Classification XGBoost with RandomBot
#> 19: regr.ft_transformer.rtdl Regression FT-Transformer with RTDL
#> 20: regr.glmnet.default Regression GLM with Default
#> 21: regr.glmnet.rbv1 Regression GLM with RandomBot
#> 22: regr.glmnet.rbv2 Regression GLM with RandomBot
#> 23: regr.mlp.rtdl Regression MLP with RTDL
#> 24: regr.ranger.default Regression Ranger with Default
#> 25: regr.ranger.rbv1 Regression Ranger with RandomBot
#> 26: regr.ranger.rbv2 Regression Ranger with RandomBot
#> 27: regr.rpart.default Regression Rpart with Default
#> 28: regr.rpart.rbv1 Regression Rpart with RandomBot
#> 29: regr.rpart.rbv2 Regression Rpart with RandomBot
#> 30: regr.svm.default Regression SVM with Default
#> 31: regr.svm.rbv1 Regression SVM with RandomBot
#> 32: regr.svm.rbv2 Regression SVM with RandomBot
#> 33: regr.tab_resnet.rtdl Regression Tabular ResNet with RTDL
#> 34: regr.xgboost.default Regression XGBoost with Default
#> 35: regr.xgboost.rbv1 Regression XGBoost with RandomBot
#> 36: regr.xgboost.rbv2 Regression XGBoost with RandomBot
#> key label
#> learner n_values
#> <char> <int>
#> 1: classif.ft_transformer 12
#> 2: classif.glmnet 2
#> 3: classif.glmnet 2
#> 4: classif.glmnet 2
#> 5: classif.mlp 7
#> 6: classif.ranger 4
#> 7: classif.ranger 6
#> 8: classif.ranger 8
#> 9: classif.rpart 3
#> 10: classif.rpart 4
#> 11: classif.rpart 4
#> 12: classif.svm 4
#> 13: classif.svm 4
#> 14: classif.svm 5
#> 15: classif.tab_resnet 9
#> 16: classif.xgboost 8
#> 17: classif.xgboost 10
#> 18: classif.xgboost 13
#> 19: regr.ft_transformer 12
#> 20: regr.glmnet 2
#> 21: regr.glmnet 2
#> 22: regr.glmnet 2
#> 23: regr.mlp 7
#> 24: regr.ranger 4
#> 25: regr.ranger 6
#> 26: regr.ranger 7
#> 27: regr.rpart 3
#> 28: regr.rpart 4
#> 29: regr.rpart 4
#> 30: regr.svm 4
#> 31: regr.svm 4
#> 32: regr.svm 5
#> 33: regr.tab_resnet 9
#> 34: regr.xgboost 8
#> 35: regr.xgboost 10
#> 36: 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
#> <char> <num> <num> <list> <lgcl>
#> 1: mtry.ratio 0.0 1 [NULL] FALSE
#> 2: replace NA NA [NULL] FALSE
#> 3: sample.fraction 0.1 1 [NULL] FALSE
#> 4: num.trees 1.0 2000 [NULL] FALSE
lts("classif.ranger.default")
#>
#> ── <TuningSpace> (classif.ranger.default): Classification Ranger with Default ──
#> id lower upper levels logscale
#> <char> <num> <num> <list> <lgcl>
#> 1: mtry.ratio 0.0 1 [NULL] FALSE
#> 2: replace NA NA [NULL] FALSE
#> 3: sample.fraction 0.1 1 [NULL] FALSE
#> 4: num.trees 1.0 2000 [NULL] FALSE