List Learners in mlr3verse
list_mlr3learners.RdLists all learners, properties, and associated packages in a table that can be filtered and queried.
Arguments
- select
character()
Passed to data.table::subset.- filter
list()
Named list of conditions to filter on, names correspond to column names in table.
Examples
list_mlr3learners(
select = c("id", "properties", "predict_types"),
filter = list(class = "surv", predict_types = "distr"))
#> id properties
#> 1: surv.akritas
#> 2: surv.blackboost weights
#> 3: surv.cforest weights
#> 4: surv.coxboost weights
#> 5: surv.coxph weights
#> 6: surv.coxtime
#> 7: surv.ctree weights
#> 8: surv.cv_coxboost weights
#> 9: surv.deephit
#> 10: surv.deepsurv
#> 11: surv.dnnsurv
#> 12: surv.flexible weights
#> 13: surv.gamboost importance,selected_features,weights
#> 14: surv.glmboost weights
#> 15: surv.kaplan missings
#> 16: surv.loghaz
#> 17: surv.mboost importance,selected_features,weights
#> 18: surv.nelson missings
#> 19: surv.obliqueRSF missings,oob_error
#> 20: surv.parametric weights
#> 21: surv.pchazard
#> 22: surv.penalized
#> 23: surv.ranger importance,oob_error,weights
#> 24: surv.rfsrc importance,missings,oob_error,weights
#> 25: surv.rpart importance,missings,selected_features,weights
#> id properties
#> predict_types
#> 1: crank,distr
#> 2: crank,distr,lp
#> 3: crank,distr
#> 4: crank,distr,lp
#> 5: crank,distr,lp
#> 6: crank,distr
#> 7: crank,distr
#> 8: crank,distr,lp
#> 9: crank,distr
#> 10: crank,distr
#> 11: crank,distr
#> 12: crank,distr,lp
#> 13: crank,distr,lp
#> 14: crank,distr,lp
#> 15: crank,distr
#> 16: crank,distr
#> 17: crank,distr,lp
#> 18: crank,distr
#> 19: crank,distr
#> 20: crank,distr,lp
#> 21: crank,distr
#> 22: crank,distr
#> 23: crank,distr
#> 24: crank,distr
#> 25: crank,distr
#> predict_types